Topic Editors

Department of Agricultural and Forestry Engineering, University of Valladolid, Campus Duques de Soria, 42004 Soria, Spain
Instituto de Computación, Facultad de Ingeniería, Universidad de la República, Montevideo, Uruguay
Department of Agricultural Engineering and Forestry, Universidad de Valladolid, Valladolid, Spain

Artificial Intelligence and Sustainable Energy Systems

Abstract submission deadline
closed (31 December 2022)
Manuscript submission deadline
closed (28 February 2023)
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Topic Artificial Intelligence and Sustainable Energy Systems book cover image

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Topic Information

Dear Colleagues,

The problems that affect humanity are numerous and occur in different areas. Energy sustainability, climate change and the effects derived from pollutants and viruses are some of the most relevant problems. The main objective of researchers is to provide solutions to these and other problems.

In recent years, the use of artificial intelligence has increased considerably. Artificial intelligence is used in different areas: energy, sustainability, medicine, health, mobility, industry, etc. Therefore, it is necessary to continue advancing in the application of artificial intelligence to the aforementioned problems. Energy is a precious commodity, and it is increasingly difficult to dispose of energy in a sustainable way. In this sense, renewable energy sources are essential, although the use of conventional energy cannot be forgotten. Therefore, sustainable energy systems, integrating renewable and non-renewable energy sources, smart systems and new business models, are crucial.

Hence, contributions are expected to involve affect artificial intelligence (in any area) and the development and evolution of sustainable energy systems.

Prof. Dr. Luis Hernández-Callejo
Dr. Sergio Nesmachnow
Dr. Sara Gallardo Saavedra
Topic Editors

Keywords

  • artificial intelligence
  • expert systems
  • machine learning
  • deep learning
  • modelling
  • prediction
  • probabilistic models
  • prediction models
  • sustainable mobility
  • smart cities
  • smart grids
  • sustainable energy systems
  • renewable energy sources
  • nonrenewable energy sources
  • electrical storage
  • green hydrogen
  • sustainable greenhouses
  • wind
  • microgrids
  • tidal
  • solar
  • biomass
  • power network
  • solar thermal
  • solar photovoltaic
  • hydraulics
  • ANN
  • neural networks, fuzzy logic, genetic algorithms
  • hybrid models based on artificial intelligence

Participating Journals

Journal Name Impact Factor CiteScore Launched Year First Decision (median) APC
Applied Sciences
applsci
2.7 4.5 2011 16.9 Days CHF 2400
Entropy
entropy
2.7 4.7 1999 20.8 Days CHF 2600
Sustainability
sustainability
3.9 5.8 2009 18.8 Days CHF 2400
Electronics
electronics
2.9 4.7 2012 15.6 Days CHF 2400
Energies
energies
3.2 5.5 2008 16.1 Days CHF 2600

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Published Papers (66 papers)

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20 pages, 13999 KiB  
Article
Synthetic Dataset of Electroluminescence Images of Photovoltaic Cells by Deep Convolutional Generative Adversarial Networks
by Héctor Felipe Mateo Romero, Luis Hernández-Callejo, Miguel Ángel González Rebollo, Valentín Cardeñoso-Payo, Victor Alonso Gómez, Hugo Jose Bello, Ranganai Tawanda Moyo and Jose Ignacio Morales Aragonés
Sustainability 2023, 15(9), 7175; https://doi.org/10.3390/su15097175 - 25 Apr 2023
Cited by 3 | Viewed by 1517
Abstract
Affordable and clean energy is one of the Sustainable Development Goals (SDG). SDG compliance and economic crises have boosted investment in solar energy as an important source of renewable generation. Nevertheless, the complex maintenance of solar plants is behind the increasing trend to [...] Read more.
Affordable and clean energy is one of the Sustainable Development Goals (SDG). SDG compliance and economic crises have boosted investment in solar energy as an important source of renewable generation. Nevertheless, the complex maintenance of solar plants is behind the increasing trend to use advanced artificial intelligence techniques, which critically depend on big amounts of data. In this work, a model based on Deep Convolutional Generative Adversarial Neural Networks (DCGANs) was trained in order to generate a synthetic dataset made of 10,000 electroluminescence images of photovoltaic cells, which extends a smaller dataset of experimentally acquired images. The energy output of the virtual cells associated with the synthetic dataset is predicted using a Random Forest regression model trained from real IV curves measured on real cells during the image acquisition process. The assessment of the resulting synthetic dataset gives an Inception Score of 2.3 and a Fréchet Inception Distance of 15.8 to the real original images, which ensures the excellent quality of the generated images. The final dataset can thus be later used to improve machine learning algorithms or to analyze patterns of solar cell defects. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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21 pages, 7112 KiB  
Article
An Adaptive Hybrid Model for Wind Power Prediction Based on the IVMD-FE-Ad-Informer
by Yuqian Tian, Dazhi Wang, Guolin Zhou, Jiaxing Wang, Shuming Zhao and Yongliang Ni
Entropy 2023, 25(4), 647; https://doi.org/10.3390/e25040647 - 12 Apr 2023
Cited by 4 | Viewed by 1446
Abstract
Accurate wind power prediction can increase the utilization rate of wind power generation and maintain the stability of the power system. At present, a large number of wind power prediction studies are based on the mean square error (MSE) loss function, which generates [...] Read more.
Accurate wind power prediction can increase the utilization rate of wind power generation and maintain the stability of the power system. At present, a large number of wind power prediction studies are based on the mean square error (MSE) loss function, which generates many errors when predicting original data with random fluctuation and non-stationarity. Therefore, a hybrid model for wind power prediction named IVMD-FE-Ad-Informer, which is based on Informer with an adaptive loss function and combines improved variational mode decomposition (IVMD) and fuzzy entropy (FE), is proposed. Firstly, the original data are decomposed into K subsequences by IVMD, which possess distinct frequency domain characteristics. Secondly, the sub-series are reconstructed into new elements using FE. Then, the adaptive and robust Ad-Informer model predicts new elements and the predicted values of each element are superimposed to obtain the final results of wind power. Finally, the model is analyzed and evaluated on two real datasets collected from wind farms in China and Spain. The results demonstrate that the proposed model is superior to other models in the performance and accuracy on different datasets, and this model can effectively meet the demand for actual wind power prediction. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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19 pages, 4868 KiB  
Article
Prediction of Faults Location and Type in Electrical Cables Using Artificial Neural Network
by Ana-Maria Moldovan and Mircea Ion Buzdugan
Sustainability 2023, 15(7), 6162; https://doi.org/10.3390/su15076162 - 3 Apr 2023
Cited by 1 | Viewed by 1267
Abstract
Detecting and locating faults in electrical cables has been a permanent concern regarding electrical power distribution systems. Over time, several techniques have been developed aiming to manage these faulty situations in an efficient way. These techniques must be fast, accurate, but, above all, [...] Read more.
Detecting and locating faults in electrical cables has been a permanent concern regarding electrical power distribution systems. Over time, several techniques have been developed aiming to manage these faulty situations in an efficient way. These techniques must be fast, accurate, but, above all, efficient. This paper develops a new approach for detecting, locating, classifying, and predicting faults, particularly in different types of short-circuits in electrical cables, based on a robust artificial neural network technique. The novelty of this approach lies in the ability of the method to predict fault’s location and type. The proposed method uses the Matlab and Simulink platform and comprises four consecutive stages. The first one is devoted to the development of the Simulink model. The second one implies a large number of simulations in order to generate the necessary dataset for training and testing the artificial neural network model (ANN). The following stage uses the ANN to classify the location and the type of potential faults. Finally, the fourth stage consists of predicting the location and the type of future faults. In order to reduce the time and the resources of the simulation process, a virtual machine is used. The study reveals the efficiency of the method, and its ability to successfully predict faults in real-world electrical power systems. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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22 pages, 1700 KiB  
Article
A Power System Timing Data Recovery Method Based on Improved VMD and Attention Mechanism Bi-Directional CNN-GRU
by Kangmin Xie, Jichun Liu and Youbo Liu
Electronics 2023, 12(7), 1590; https://doi.org/10.3390/electronics12071590 - 28 Mar 2023
Cited by 2 | Viewed by 1257
Abstract
The temporal data of the power system are expanding with the growth of the power system and the proliferation of automated equipment. However, data loss may arise during the acquisition, measurement, transmission, and storage of temporal data. To address the insufficiency of temporal [...] Read more.
The temporal data of the power system are expanding with the growth of the power system and the proliferation of automated equipment. However, data loss may arise during the acquisition, measurement, transmission, and storage of temporal data. To address the insufficiency of temporal data in the power system, this study proposes a sequence-to-sequence (Seq2Seq) architecture to restore power system temporal data. This architecture comprises a radial convolutional neural unit (CNN) network and a gated recurrent unit (GRU) network. Specifically, to account for the periodicity and volatility of temporal data, VMD is employed to decompose the time series data output into components of different frequencies. CNN is utilized to extract the spatial characteristics of temporal data. At the same time, Seq2Seq is employed to reconstruct each component based on introducing a feature timing and multi-model combination triple attention mechanism. The feature attention mechanism calculates the contribution rate of each feature quantity and independently mines the correlation between the time series data output and each feature value. The temporal attention mechanism autonomously extracts historical–critical moment information. A multi-model combination attention mechanism is introduced, and the missing data repair value is obtained after modeling the combination of data on both sides of the missing data. Recovery experiments are conducted based on actual data, and the method’s effectiveness is verified by comparison with other methods. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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23 pages, 7475 KiB  
Article
An Improved Deep Reinforcement Learning Method for Dispatch Optimization Strategy of Modern Power Systems
by Suwei Zhai, Wenyun Li, Zhenyu Qiu, Xinyi Zhang and Shixi Hou
Entropy 2023, 25(3), 546; https://doi.org/10.3390/e25030546 - 22 Mar 2023
Cited by 1 | Viewed by 1728
Abstract
As a promising information theory, reinforcement learning has gained much attention. This paper researches a wind-storage cooperative decision-making strategy based on dueling double deep Q-network (D3QN). Firstly, a new wind-storage cooperative model is proposed. Besides wind farms, energy storage systems, and external power [...] Read more.
As a promising information theory, reinforcement learning has gained much attention. This paper researches a wind-storage cooperative decision-making strategy based on dueling double deep Q-network (D3QN). Firstly, a new wind-storage cooperative model is proposed. Besides wind farms, energy storage systems, and external power grids, demand response loads are also considered, including residential price response loads and thermostatically controlled loads (TCLs). Then, a novel wind-storage cooperative decision-making mechanism is proposed, which combines the direct control of TCLs with the indirect control of residential price response loads. In addition, a kind of deep reinforcement learning algorithm called D3QN is utilized to solve the wind-storage cooperative decision-making problem. Finally, the numerical results verify the effectiveness of D3QN for optimizing the decision-making strategy of a wind-storage cooperation system. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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20 pages, 8946 KiB  
Article
Probability Density Forecasting of Wind Power Based on Transformer Network with Expectile Regression and Kernel Density Estimation
by Haoyi Xiao, Xiaoxia He and Chunli Li
Electronics 2023, 12(5), 1187; https://doi.org/10.3390/electronics12051187 - 1 Mar 2023
Cited by 6 | Viewed by 1729
Abstract
A comprehensive and accurate wind power forecast assists in reducing the operational risk of wind power generation, improves the safety and stability of the power system, and maintains the balance of wind power generation. Herein, a hybrid wind power probabilistic density forecasting approach [...] Read more.
A comprehensive and accurate wind power forecast assists in reducing the operational risk of wind power generation, improves the safety and stability of the power system, and maintains the balance of wind power generation. Herein, a hybrid wind power probabilistic density forecasting approach based on a transformer network combined with expectile regression and kernel density estimation (Transformer-ER-KDE) is methodically established. The wind power prediction results of various levels are exploited as the input of kernel density estimation, and the optimal bandwidth is achieved by employing leave-one-out cross-validation to arrive at the complete probability density prediction curve. In order to more methodically assess the predicted wind power results, two sets of evaluation criteria are constructed, including evaluation metrics for point estimation and interval prediction. The wind power generation dataset from the official website of the Belgian grid company Elia is employed to validate the proposed approach. The experimental results reveal that the proposed Transformer-ER-KDE method outperforms mainstream recurrent neural network models in terms of point estimation error. Further, the suggested approach is capable of more accurately capturing the uncertainty in the forecasting of wind power through the construction of accurate prediction intervals and probability density curves. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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12 pages, 3404 KiB  
Article
Lightweight Network-Based Surface Defect Detection Method for Steel Plates
by Changqing Wang, Maoxuan Sun, Yuan Cao, Kunyu He, Bei Zhang, Zhonghao Cao and Meng Wang
Sustainability 2023, 15(4), 3733; https://doi.org/10.3390/su15043733 - 17 Feb 2023
Cited by 6 | Viewed by 1758
Abstract
This article proposes a lightweight YOLO-ACG detection algorithm that balances accuracy and speed, which improves on the classification errors and missed detections present in existing steel plate defect detection algorithms. To highlight the key elements of the desired area of surface flaws in [...] Read more.
This article proposes a lightweight YOLO-ACG detection algorithm that balances accuracy and speed, which improves on the classification errors and missed detections present in existing steel plate defect detection algorithms. To highlight the key elements of the desired area of surface flaws in steel plates, a void space convolutional pyramid pooling model is applied to the backbone network. This model improves the fusion of high- and low-level semantic information by designing feature pyramid networks with embedded spatial attention. According to the experimental findings, the suggested detection algorithm enhances the mapped value by about 4% once compared to the YOLOv4-Ghost detection algorithm on the homemade data set. Additionally, the real-time detection speed reaches about 103FPS, which is about 7FPS faster than the YOLOv4-Ghost detection algorithm, and the detection capability of steel surface defects is significantly enhanced to meet the needs of real-time detection of realistic scenes in the mobile terminal. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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16 pages, 4165 KiB  
Article
TCNformer Model for Photovoltaic Power Prediction
by Shipeng Liu, Dejun Ning and Jue Ma
Appl. Sci. 2023, 13(4), 2593; https://doi.org/10.3390/app13042593 - 17 Feb 2023
Cited by 1 | Viewed by 1320
Abstract
Despite the growing capabilities of the short-term prediction of photovoltaic power, we still face two challenges to longer time-range predictions: error accumulation and long-term time series feature extraction. In order to improve the longer time range prediction accuracy of photovoltaic power, this paper [...] Read more.
Despite the growing capabilities of the short-term prediction of photovoltaic power, we still face two challenges to longer time-range predictions: error accumulation and long-term time series feature extraction. In order to improve the longer time range prediction accuracy of photovoltaic power, this paper proposes a seq2seq prediction model TCNformer, which outperforms other state-of-the-art (SOTA) algorithms by introducing variable selection (VS), long- and short-term time series feature extraction (LSTFE), and one-step temporal convolutional network (TCN) decoding. A VS module employs correlation analysis and periodic analysis to separate the time series correlation information, LSTFE extracts multiple time series features from time series data, and one-step TCN decoding realizes generative predictions. We demonstrate here that TCNformer has the lowest mean squared error (MSE), mean absolute error (MAE) and mean absolute percentage error (MAPE) in contrast to the other algorithms in the field of the short-term prediction of photovoltaic power, and furthermore, the effectiveness of each module has been verified through ablation experiments. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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27 pages, 795 KiB  
Article
A Two-Stage Bilateral Matching Study of Teams-Technology Talents in New R&D Institutions Based on Prospect Theory
by Lin Jiang and Biyun Chen
Sustainability 2023, 15(4), 3494; https://doi.org/10.3390/su15043494 - 14 Feb 2023
Viewed by 1169
Abstract
This study considers two-stage bilateral matching of teams and scientific and technological talents in new R&D organizations and proposes a two-stage dual-objective bilateral matching method based on prospect theory. The matching of teams and scientific and technological talent in new R&D institutions is [...] Read more.
This study considers two-stage bilateral matching of teams and scientific and technological talents in new R&D organizations and proposes a two-stage dual-objective bilateral matching method based on prospect theory. The matching of teams and scientific and technological talent in new R&D institutions is divided into two stages: elimination matching in the first stage and selection matching in the second stage. In the first stage, the evaluation index of the team to talent and the cost index of talent are constructed, the dual reference points of peer and expectation are set for evaluating talent, and the bottom-line reference points are set for talent cost. The comprehensive prospect value in the first stage is calculated based on prospect theory, and the matching in the first stage is completed based on the dual-objective optimization model with the highest evaluation value and the lowest cost value. In the second stage, using the matching results of the first stage, the team evaluates the talent again, while the talent ranks the team to obtain the satisfaction value, and completes the second stage of bilateral matching based on prospect theory and the dual-objective optimization model with the highest evaluation value and the highest satisfaction value. Finally, a case study and method comparison show that the proposed method is feasible and effective. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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10 pages, 2910 KiB  
Article
Comparative Estimation of Electrical Characteristics of a Photovoltaic Module Using Regression and Artificial Neural Network Models
by Jonghwan Lee and Yongwoo Kim
Electronics 2022, 11(24), 4228; https://doi.org/10.3390/electronics11244228 - 19 Dec 2022
Cited by 3 | Viewed by 1229
Abstract
Accurate modeling of photovoltaic (PV) modules under outdoor conditions is essential to facilitate the optimal design and assessment of PV systems. As an alternative model to the translation equations based on regression methods, various data-driven models have been adopted to estimate the current–voltage [...] Read more.
Accurate modeling of photovoltaic (PV) modules under outdoor conditions is essential to facilitate the optimal design and assessment of PV systems. As an alternative model to the translation equations based on regression methods, various data-driven models have been adopted to estimate the current–voltage (I–V) characteristics of a photovoltaic module under varying operation conditions. In this paper, artificial neural network (ANN) models are compared with the regression models for five parameters of a single diode solar cell. In the configuration of the proposed PV models, the five parameters are predicted by regression and neural network models, and these parameters are put into an explicit expression such as the Lambert W function. The multivariate regression parameters are determined by using the least square method (LSM). The ANN model is constructed by using a four-layer, feed-forward neural network, in which the inputs are temperature and solar irradiance, and the outputs are the five parameters. By training an experimental dataset, the ANN model is built and utilized to predict the five parameters by reading the temperature and solar irradiance. The performance of the regression and ANN models is evaluated by using root mean squared error (RMSE) and mean absolute percentage error (MAPE). A comparative study of the regression and ANN models shows that the performance of the ANN models is better than the regression models. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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25 pages, 6132 KiB  
Article
Prediction of Solid Conversion Process in Direct Reduction Iron Oxide Using Machine Learning
by Masih Hosseinzadeh, Hossein Mashhadimoslem, Farid Maleki and Ali Elkamel
Energies 2022, 15(24), 9276; https://doi.org/10.3390/en15249276 - 7 Dec 2022
Cited by 5 | Viewed by 2173
Abstract
The direct reduction process has been developed and investigated in recent years due to less pollution than other methods. In this work, the first direct reduction iron oxide (DRI) modeling has been developed using artificial neural networks (ANN) algorithms such as the multilayer [...] Read more.
The direct reduction process has been developed and investigated in recent years due to less pollution than other methods. In this work, the first direct reduction iron oxide (DRI) modeling has been developed using artificial neural networks (ANN) algorithms such as the multilayer perceptron (MLP) and radial basis function (RBF) models. A DRI operation takes place inside the shaft furnace. A shaft furnace reactor is a gas-solid reactor that transforms iron oxide particles into sponge iron. Because of its low environmental pollution, the MIDREX process, one of the DRI procedures, has received much attention in recent years. The main purpose of the shaft furnace is to achieve the desired percentage of solid conversion output from the furnace. The network parameters were optimized, and an algorithm was developed to achieve an optimum NN model. The results showed that the MLP network has a minimum squared error (MSE) of 8.95 × 10−6, which is the lowest error compared to the RBF network model. The purpose of the study was to identify the shaft furnace solid conversion using machine learning methods without solving nonlinear equations. Another advantage of this research is that the running speed is 3.5 times the speed of mathematical modeling. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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29 pages, 9227 KiB  
Article
Artificial Intelligence (AI)-Based Occupant-Centric Heating Ventilation and Air Conditioning (HVAC) Control System for Multi-Zone Commercial Buildings
by Alperen Yayla, Kübra Sultan Świerczewska, Mahmut Kaya, Bahadır Karaca, Yusuf Arayici, Yunus Emre Ayözen and Onur Behzat Tokdemir
Sustainability 2022, 14(23), 16107; https://doi.org/10.3390/su142316107 - 2 Dec 2022
Cited by 8 | Viewed by 5909
Abstract
Buildings are responsible for almost half of the world’s energy consumption, and approximately 40% of total building energy is consumed by the heating ventilation and air conditioning (HVAC) system. The inability of traditional HVAC controllers to respond to sudden changes in occupancy and [...] Read more.
Buildings are responsible for almost half of the world’s energy consumption, and approximately 40% of total building energy is consumed by the heating ventilation and air conditioning (HVAC) system. The inability of traditional HVAC controllers to respond to sudden changes in occupancy and environmental conditions makes them energy inefficient. Despite the oversimplified building thermal response models and inexact occupancy sensors of traditional building automation systems, investigations into a more efficient and effective sensor-free control mechanism have remained entirely inadequate. This study aims to develop an artificial intelligence (AI)-based occupant-centric HVAC control mechanism for cooling that continually improves its knowledge to increase energy efficiency in a multi-zone commercial building. The study is carried out using two-year occupancy and environmental conditions data of a shopping mall in Istanbul, Turkey. The research model consists of three steps: prediction of hourly occupancy, development of a new HVAC control mechanism, and comparison of the traditional and AI-based control systems via simulation. After determining the attributions for occupancy in the mall, hourly occupancy prediction is made using real data and an artificial neural network (ANN). A sensor-free HVAC control algorithm is developed with the help of occupancy data obtained from the previous stage, building characteristics, and real-time weather forecast information. Finally, a comparison of traditional and AI-based HVAC control mechanisms is performed using IDA Indoor Climate and Energy (ICE) simulation software. The results show that applying AI for HVAC operation achieves savings of a minimum of 10% energy consumption while providing a better thermal comfort level to occupants. The findings of this study demonstrate that the proposed approach can be a very advantageous tool for sustainable development and also used as a standalone control mechanism as it improves. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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15 pages, 3603 KiB  
Article
Semantic Segmentation Algorithm-Based Calculation of Cloud Shadow Trajectory and Cloud Speed
by Shitao Wang, Mingjian Sun and Yi Shen
Energies 2022, 15(23), 8925; https://doi.org/10.3390/en15238925 - 25 Nov 2022
Cited by 1 | Viewed by 1067
Abstract
Cloud covering is an important factor affecting solar radiation and causes fluctuations in solar energy production. Therefore, real-time recognition and the prediction of cloud covering and the adjustment of the angle of photovoltaic panels to improve power generation are important research areas in [...] Read more.
Cloud covering is an important factor affecting solar radiation and causes fluctuations in solar energy production. Therefore, real-time recognition and the prediction of cloud covering and the adjustment of the angle of photovoltaic panels to improve power generation are important research areas in the field of photovoltaic power generation. In this study, several methods, namely, the principle of depth camera measurement distance, semantic segmentation algorithm, and long- and short-term memory (LSTM) network were combined for cloud observation. The semantic segmentation algorithm was applied to identify and extract the cloud contour lines, determine the feature points, and calculate the cloud heights and geographic locations of the cloud shadows. The LSTM algorithm was used to predict the trajectory and speed of the cloud movement, achieve accurate and real-time detection, and track the clouds and the sun. Based on the results of these methods, the shadow area of the cloud on the ground was calculated. The recursive neural LSTM network was also used to predict the track and moving speed of the clouds according to the cloud centroid data of the cloud images at different times. The findings of this study can provide insights to establish a low-cost intelligent monitoring predicting system for cloud covering and power generation. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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13 pages, 1326 KiB  
Article
Maritime Autonomous Surface Ships: Problems and Challenges Facing the Regulatory Process
by Mohamad Issa, Adrian Ilinca, Hussein Ibrahim and Patrick Rizk
Sustainability 2022, 14(23), 15630; https://doi.org/10.3390/su142315630 - 24 Nov 2022
Cited by 10 | Viewed by 7529
Abstract
Technological innovation constantly transforms and redefines the human element’s position inside complex socio-technical systems. Autonomous operations are in various phases of development and practical deployment across several transport domains, with marine operations still in their infancy. This article discusses current trends in developing [...] Read more.
Technological innovation constantly transforms and redefines the human element’s position inside complex socio-technical systems. Autonomous operations are in various phases of development and practical deployment across several transport domains, with marine operations still in their infancy. This article discusses current trends in developing autonomous vessels and some of the most recent initiatives worldwide. It also investigates the individual and combined effects of maritime autonomous surface ships (MASS) on regulations, technology, and sectors in reaction to the new marine paradigm change. Other essential topics, such as safety, security, jobs, training, and legal and ethical difficulties, are also considered to develop a solution for efficient, dependable, safe, and sustainable shipping in the near future. Finally, it is advised that holistic approaches to building the technology and regulatory framework be used and that communication and cooperation among various stakeholders based on mutual understanding are essential for the MASS to arrive in the maritime industry successfully. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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16 pages, 2455 KiB  
Article
Solar Irradiance Probabilistic Forecasting Using Machine Learning, Metaheuristic Models and Numerical Weather Predictions
by Vateanui Sansine, Pascal Ortega, Daniel Hissel and Marania Hopuare
Sustainability 2022, 14(22), 15260; https://doi.org/10.3390/su142215260 - 17 Nov 2022
Cited by 8 | Viewed by 1730
Abstract
Solar-power-generation forecasting tools are essential for microgrid stability, operation, and planning. The prediction of solar irradiance (SI) usually relies on the time series of SI and other meteorological data. In this study, the considered microgrid was a combined cold- and power-generation system, located [...] Read more.
Solar-power-generation forecasting tools are essential for microgrid stability, operation, and planning. The prediction of solar irradiance (SI) usually relies on the time series of SI and other meteorological data. In this study, the considered microgrid was a combined cold- and power-generation system, located in Tahiti. Point forecasts were obtained using a particle swarm optimization (PSO) algorithm combined with three stand-alone models: XGboost (PSO-XGboost), the long short-term memory neural network (PSO-LSTM), and the gradient boosting regression algorithm (PSO-GBRT). The implemented daily SI forecasts relied on an hourly time-step. The input data were composed of outputs from the numerical forecasting model AROME (Météo France) combined with historical meteorological data. Our three hybrid models were compared with other stand-alone models, namely, artificial neural network (ANN), convolutional neural network (CNN), random forest (RF), LSTM, GBRT, and XGboost. The probabilistic forecasts were obtained by mapping the quantiles of the hourly residuals, which enabled the computation of 38%, 68%, 95%, and 99% prediction intervals (PIs). The experimental results showed that PSO-LSTM had the best accuracy for day-ahead solar irradiance forecasting compared with the other benchmark models, through overall deterministic and probabilistic metrics. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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19 pages, 3204 KiB  
Article
System Frequency Control Method Driven by Deep Reinforcement Learning and Customer Satisfaction for Thermostatically Controlled Load
by Rusi Chen, Haiguang Liu, Chengquan Liu, Guangzheng Yu, Xuan Yang and Yue Zhou
Energies 2022, 15(21), 7866; https://doi.org/10.3390/en15217866 - 24 Oct 2022
Viewed by 1128
Abstract
The intermittence and fluctuation of renewable energy aggravate the power fluctuation of the power grid and pose a severe challenge to the frequency stability of the power system. Thermostatically controlled loads can participate in the frequency regulation of the power grid due to [...] Read more.
The intermittence and fluctuation of renewable energy aggravate the power fluctuation of the power grid and pose a severe challenge to the frequency stability of the power system. Thermostatically controlled loads can participate in the frequency regulation of the power grid due to their flexibility. Aiming to solve the problem of the traditional control methods, which have limited adjustment ability, and to have a positive influence on customers, a deep reinforcement learning control strategy based on the framework of soft actor–critic is proposed, considering customer satisfaction. Firstly, the energy storage index and the discomfort index of different users are defined. Secondly, the fuzzy comprehensive evaluation method is applied to evaluate customer satisfaction. Then, the multi-agent models of thermostatically controlled loads are established based on the soft actor–critic algorithm. The models are trained by using the local information of thermostatically controlled loads, and the comprehensive evaluation index fed back by users and the frequency deviation. After training, each agent can realize the cooperative response of thermostatically controlled loads to the system frequency only by relying on the local information. The simulation results show that the proposed strategy can not only reduce the frequency fluctuation, but also improve customer satisfaction. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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23 pages, 2503 KiB  
Systematic Review
Identifying Key Components in Implementation of Internet of Energy (IoE) in Iran with a Combined Approach of Meta-Synthesis and Structural Analysis: A Systematic Review
by Mir Hamid Taghavi, Peyman Akhavan, Rouhollah Ahmadi and Ali Bonyadi Naeini
Sustainability 2022, 14(20), 13180; https://doi.org/10.3390/su142013180 - 14 Oct 2022
Viewed by 1374
Abstract
The increasing consumption of energy and the numerous obstacles in the way of its extraction, including diminishing fossil fuels and the turn towards renewable energies, environmental changes, a tendency towards systems of information networks, rising costs of energy and advancement of technology have [...] Read more.
The increasing consumption of energy and the numerous obstacles in the way of its extraction, including diminishing fossil fuels and the turn towards renewable energies, environmental changes, a tendency towards systems of information networks, rising costs of energy and advancement of technology have made the need for new technologies aimed at efficient management of energy more imminent. The Internet of Energy (IoE) technology has been recognized as a novel and efficient strategy that provides the necessary tools for optimal energy management. The present study was carried out with the purpose of identifying key components in implementation of IoE in Iran. This study is practical in its goal and descriptive-explorative in its methodology. First, the data were categorized using the qualitative method of meta-synthesis and using the Sandelowski and Barroso method. The statistical population of the study was the scholarly finding of 2010–2021 and 55 papers were sampled from the published works. The kappa coefficient was used to determine reliability and quality control. The kappa coefficient calculated with SPSS equals 0.87, which falls in the “excellent” category. Second, the frequency and importance of each component was determined using the Shannon entropy technique. The purpose of this method is to measure the weight or importance of each component based on frequency and to identify the key components. Third, the MICMAC structural analysis method was used to evaluate the influence/dependence of components by eight experts in the field of energy and determine strategic components. The purpose of this step is to compare the results with the results of the second step of the research. The results show that 82 indicators play a role in implementation of the concept of IoE; these indicators can be divided into ten axial categories of rules and regulations, individual and human factors, funding, technological infrastructure, cultural and social factors, security factors, technological factors, knowledge factors, learning style, and management factors. In the Shannon entropy method, technological infrastructure, management factors, and rules and regulations are the most significant, respectively. In MICMAC structural analysis, the components of managerial factors, technological infrastructure, and financing have the largest share in influence and dependence, respectively. Conclusion: The two components of management factors and technological infrastructure can be considered as key and strategic components in implementation of IoE in Iran. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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18 pages, 3361 KiB  
Article
Neuro-Cybernetic System for Forecasting Electricity Consumption in the Bulgarian National Power System
by Kostadin Yotov, Emil Hadzhikolev, Stanka Hadzhikoleva and Stoyan Cheresharov
Sustainability 2022, 14(17), 11074; https://doi.org/10.3390/su141711074 - 5 Sep 2022
Cited by 1 | Viewed by 1424
Abstract
Making forecasts for the development of a given process over time, which depends on many factors, is in some cases a difficult task. The choice of appropriate methods—mathematical, statistical, or artificial intelligence methods—is also not obvious, given their great variety. This paper presented [...] Read more.
Making forecasts for the development of a given process over time, which depends on many factors, is in some cases a difficult task. The choice of appropriate methods—mathematical, statistical, or artificial intelligence methods—is also not obvious, given their great variety. This paper presented a model of a forecasting system by comparing the errors in the use of time series on the one hand, and artificial neural networks on the other. The model aims at multifactor predictions based on forecast data on significant factors, which were obtained by automated testing of different methods and selection of the methods with the highest accuracy. Successful experiments were conducted to forecast energy consumption in Bulgaria, including for household consumption; industry consumption, the public sector and services; and total final energy consumption. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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27 pages, 4436 KiB  
Systematic Review
Solar Panels Dirt Monitoring and Cleaning for Performance Improvement: A Systematic Review on Smart Systems
by Benjamin Oluwamuyiwa Olorunfemi, Omolola A. Ogbolumani and Nnamdi Nwulu
Sustainability 2022, 14(17), 10920; https://doi.org/10.3390/su141710920 - 1 Sep 2022
Cited by 19 | Viewed by 10279
Abstract
The advancement in technology to manage energy generation using solar panels has proved vital for increased reliability and reduced cost. Solar panels emit no pollution while producing electricity as a renewable energy source. However, the solar panel is adversely affected by dirt, a [...] Read more.
The advancement in technology to manage energy generation using solar panels has proved vital for increased reliability and reduced cost. Solar panels emit no pollution while producing electricity as a renewable energy source. However, the solar panel is adversely affected by dirt, a major environmental factor affecting energy production. The intensity of light falling on the solar panel is reduced when dirt accumulates on the surface. This, in turn, lowers the output of electrical energy generated by the solar panel. Since cleansing the solar panel is essential, constant monitoring and evaluation of these processes are necessary to optimize them. This emphasizes the importance of using smart systems to monitor dirt and clean solar panels to improve their performance. The paper tries to verify the existence and the degree of research interest in this topic and seeks to evaluate the impact of smart systems to detect dirt conditions and clean solar panels compared to autonomous and manual technology. Research on smart systems for addressing dirt accumulation on solar panels was conducted taking into account efficiency, accuracy, complexity, and reliability, initial and running cost. Overall, real-time monitoring and cleaning of the solar panel improved its output power with integrated smart systems. It helps users get real-time updates of the solar panel’s condition and control actions from distant locations. A critical limitation of this research is the insufficient empirical analysis of existing smart systems, which should be thoroughly examined to allow further generalization of theoretical findings. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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19 pages, 6462 KiB  
Article
Intelligent Deep-Q-Network-Based Energy Management for an Isolated Microgrid
by Bao Chau Phan, Meng-Tse Lee and Ying-Chih Lai
Appl. Sci. 2022, 12(17), 8721; https://doi.org/10.3390/app12178721 - 31 Aug 2022
Cited by 2 | Viewed by 1574
Abstract
The development of hybrid renewable energy systems (HRESs) can be the most feasible solution for a stable, environment-friendly, and cost-effective power generation, especially in rural and island territories. In this studied HRES, solar and wind energy are used as the major resources. Moreover, [...] Read more.
The development of hybrid renewable energy systems (HRESs) can be the most feasible solution for a stable, environment-friendly, and cost-effective power generation, especially in rural and island territories. In this studied HRES, solar and wind energy are used as the major resources. Moreover, the electrolyzed hydrogen is utilized to store energy for the operation of a fuel cell. In case of insufficiency, battery and fuel cell are storage systems that supply energy, while a diesel generator adds a backup system to meet the load demand under bad weather conditions. An isolated HRES energy management system (EMS) based on a Deep Q Network (DQN) is introduced to ensure the reliable and efficient operation of the system. A DQN can deal with the problem of continuous state spaces and manage the dynamic behavior of hybrid systems without exact mathematical models. Following the power consumption data from Basco island of the Philippines, HOMER software is used to calculate the capacity of each component in the proposed power plant. In MATLAB/Simulink, the plant and its DQN-based EMS are simulated. Under different load profile scenarios, the proposed method is compared to the convectional dispatch (CD) control for a validation. Based on the outstanding performances with fewer fuel consumption, DQN is a very powerful and potential method for energy management. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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15 pages, 7289 KiB  
Article
Development of an Intelligent Solution for the Optimization of Hybrid Energy Systems
by Djamel Saba, Fahima Hajjej, Omar Cheikhrouhou, Youcef Sahli, Abdelkader Hadidi and Habib Hamam
Appl. Sci. 2022, 12(17), 8397; https://doi.org/10.3390/app12178397 - 23 Aug 2022
Cited by 3 | Viewed by 1692
Abstract
This paper presents a proposal for the development of a new intelligent solution for the optimization of hybrid energy systems. This solution is of great importance for installers of hybrid energy systems, as it helps them obtain the best configuration of the hybrid [...] Read more.
This paper presents a proposal for the development of a new intelligent solution for the optimization of hybrid energy systems. This solution is of great importance for installers of hybrid energy systems, as it helps them obtain the best configuration of the hybrid energy system (efficient and less expensive). In this solution, it is sufficient to enter the name of the location of the hybrid energy system that we want to install; after that, the solution will show the name of the best technology from which the optimal configuration of this system can be obtained. To accomplish this goal, the study relied on the ontology approach for two reasons, one of which is related to the nature of hybrid systems, because it is characterized by a large amount of information that requires good structuring, and the second reason is the interaction of hybrid energy systems with the external environment (climate, site characteristics). Afterward, to develop the knowledge base of the ontology, many steps were followed, the first of which is related to a detailed study of the existing one and the extraction of the basic elements, such as the concepts and the relations between them, followed by the development of the rules of intelligent reasoning, which is an interaction between the elements of the ontology through which all possible cases are treated. The “Protégé” software was used to edit these elements and perform the simulation process to show the results of the developed solution. Finally, the paper includes a case study, and the results show the importance of the developed solution, and it is open to future developments. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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16 pages, 4894 KiB  
Article
A Hybrid Generative Adversarial Network Model for Ultra Short-Term Wind Speed Prediction
by Qingyuan Wang, Longnv Huang, Jiehui Huang, Qiaoan Liu, Limin Chen, Yin Liang, Peter X. Liu and Chunquan Li
Sustainability 2022, 14(15), 9021; https://doi.org/10.3390/su14159021 - 22 Jul 2022
Cited by 1 | Viewed by 1385
Abstract
To improve the accuracy of ultra-short-term wind speed prediction, a hybrid generative adversarial network model (HGANN) is proposed in this paper. Firstly, to reduce the noise of the wind sequence, the raw wind data are decomposed using complete ensemble empirical mode decomposition with [...] Read more.
To improve the accuracy of ultra-short-term wind speed prediction, a hybrid generative adversarial network model (HGANN) is proposed in this paper. Firstly, to reduce the noise of the wind sequence, the raw wind data are decomposed using complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN). Then the decomposed modalities are entered into the HGANN network for prediction. HGANN is a continuous game between the generator and the discriminator, which in turn allows the generator to learn the distribution of the wind data and make predictions about it. Notably, we developed the optimized broad learning system (OBLS) as a generator for the HGANN network, which can improve the generalization ability and error convergence of HGANN. In addition, improved particle swarm optimization (IPSO) was used to optimize the hyperparameters of OBLS. To validate the performance of the HGANN model, experiments were conducted using wind sequences from different regions and at different times. The experimental results show that our model outperforms other cutting-edge benchmark models in single-step and multi-step forecasts. This demonstrates not only the accuracy and robustness of the proposed model but also the applicability of our model to more general environments for wind speed prediction. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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26 pages, 8965 KiB  
Article
Static Analysis and Optimization of Voltage and Reactive Power Regulation Systems in the HV/MV Substation with Electronic Transformer Tap-Changers
by Jarosław Korpikiewicz and Mostefa Mohamed-Seghir
Energies 2022, 15(13), 4773; https://doi.org/10.3390/en15134773 - 29 Jun 2022
Cited by 4 | Viewed by 1808
Abstract
The quality of electricity is a very important indicator. The durability and reliable operation of all connected devices depend on the quality of the network voltage. Rapid changes in loads, changes in network connections and the presence of uncontrolled energy sources require the [...] Read more.
The quality of electricity is a very important indicator. The durability and reliable operation of all connected devices depend on the quality of the network voltage. Rapid changes in loads, changes in network connections and the presence of uncontrolled energy sources require the development of new voltage regulation systems. This requires voltage regulation systems capable of responding quickly to sudden voltage changes. In substations with control transformers, it is possible thanks to the use of semiconductor tap changers. Moreover, voltage regulation and reactive power compensation systems should be built as one system. This is due to the close dependence of voltage and reactive power in the network node. Therefore, it was proposed to use artificial intelligence methods to build a new voltage regulation and reactive power compensation system using all measurement voltages of network nodes. In the first stage of the research, active and reactive powers, as well as the voltage of the reference node, were selected for 6420 periods of the mains voltage. The simulation results were compared for the classic voltage regulation system with semiconductor tap changers and the evolution algorithm based on voltage measurements from the entire MV network. A significant improvement in the quality of voltage regulation with the use of an evolutionary algorithm was demonstrated. Then, a second set of input data with increased values of reactive power was generated. The results of the evolutionary algorithm after the application of the classic, independent reactive power compensation system and two-criteria optimization were compared. It has been shown that only the two-criteria optimization algorithm keeps both |tgφ| within the acceptable range and the quality of voltage regulation is the best. The article compares different working algorithms for semiconductor tap changers. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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16 pages, 3022 KiB  
Article
An Improved Generating Energy Prediction Method Based on Bi-LSTM and Attention Mechanism
by Bo He, Runze Ma, Wenwei Zhang, Jun Zhu and Xingyuan Zhang
Electronics 2022, 11(12), 1885; https://doi.org/10.3390/electronics11121885 - 15 Jun 2022
Cited by 5 | Viewed by 1712
Abstract
The energy generated by a photovoltaic power station is affected by environmental factors, and the prediction of the generating energy would be helpful for power grid scheduling. Recently, many power generation prediction models (PGPM) based on machine learning have been proposed, but few [...] Read more.
The energy generated by a photovoltaic power station is affected by environmental factors, and the prediction of the generating energy would be helpful for power grid scheduling. Recently, many power generation prediction models (PGPM) based on machine learning have been proposed, but few existing methods use the attention mechanism to improve the prediction accuracy of generating energy. In the paper, a PGPM based on the Bi-LSTM model and attention mechanism was proposed. Firstly, the environmental factors with respect to the generating energy were selected through the Pearson coefficient, and then the principle and implementation of the proposed PGPM were detailed. Finally, the performance of the proposed PGPM was evaluated through an actual data set collected from a photovoltaic power station in Suzhou, China. The experimental results showed that the prediction error of proposed PGPM was only 8.6 kWh, and the fitting accuracy was more than 0.99, which is better than existing methods. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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25 pages, 10122 KiB  
Article
Feature Selection to Predict LED Light Energy Consumption with Specific Light Recipes in Closed Plant Production Systems
by Martín Montes Rivera, Nivia Escalante-Garcia, José Alonso Dena-Aguilar, Ernesto Olvera-Gonzalez and Paulino Vacas-Jacques
Appl. Sci. 2022, 12(12), 5901; https://doi.org/10.3390/app12125901 - 9 Jun 2022
Cited by 4 | Viewed by 2368
Abstract
The use of closed growth environments, such as greenhouses, plant factories, and vertical farms, represents a sustainable alternative for fresh food production. Closed plant production systems (CPPSs) allow growing of any plant variety, no matter the year’s season. Artificial lighting plays an essential [...] Read more.
The use of closed growth environments, such as greenhouses, plant factories, and vertical farms, represents a sustainable alternative for fresh food production. Closed plant production systems (CPPSs) allow growing of any plant variety, no matter the year’s season. Artificial lighting plays an essential role in CPPSs as it promotes growth by providing optimal conditions for plant development. Nevertheless, it is a model with a high demand for electricity, which is required for artificial radiation systems to enhance the developing plants. A high percentage (40% to 50%) of the costs in CPPSs point to artificial lighting systems. Due to this, lighting strategies are essential to improve sustainability and profitability in closed plant production systems. However, no tools have been applied in the literature to contribute to energy savings in LED-type artificial radiation systems through the configuration of light recipes (wavelengths combination. For CPPS to be cost-effective and sustainable, a pre-evaluation of energy consumption for plant cultivation must consider. Artificial intelligence (AI) methods integrated into the prediction crucial variables such as each input-variable light color or specific wavelengths like red, green, blue, and white along with light intensity (quantity), frequency (pulsed light), and duty cycle. This paper focuses on the feature-selection stage, in which a regression model is trained to predict energy consumption in LED lights with specific light recipes in CPPSs. This stage is critical because it identifies the most representative features for training the model, and the other stages depend on it. These tools can enable further in-depth analysis of the energy savings that can be obtained with light recipes and pulsed and continuous operation light modes in artificial LED lighting systems. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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16 pages, 13620 KiB  
Article
Electric Vehicle Fire Trace Recognition Based on Multi-Task Semantic Segmentation
by Jiankun Pu and Wei Zhang
Electronics 2022, 11(11), 1738; https://doi.org/10.3390/electronics11111738 - 30 May 2022
Cited by 3 | Viewed by 1470
Abstract
Conflagration is the major safety issue of electric vehicles (EVs). Due to their well-kept appearance and structure, which demonstrate salient visual changes after combustion, EV bodies are recognized as an important basis for on-spot inspection of burnt EVs and make application using semantic [...] Read more.
Conflagration is the major safety issue of electric vehicles (EVs). Due to their well-kept appearance and structure, which demonstrate salient visual changes after combustion, EV bodies are recognized as an important basis for on-spot inspection of burnt EVs and make application using semantic segmentation possible. The combination of deep learning-based semantic segmentation and recognition of visual traces of burnt EVs would provide preliminary analytical results of fire spread trends and output status descriptions of burnt EVs for further investigation. In this paper, a dataset of image traces of burnt EVs was built, and a two-branch network structure that splits the whole task into two sub-tasks separately concentrated on foreground extraction and severity segmentation is proposed. The proposed network is trained on the dataset via the transfer learning method and is tested using 5-fold cross validation. The foreground extraction branch achieved a mean intersection over union (mIoU) of 95.16% in the burnt EV foreground extraction task, and the burnt severity branch achieved a mIoU of 66.96% for the severity segmentation task. By jointly training two branches and applying a foreground mask to 3-class severity output, the mIoU was improved to 68.92%. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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25 pages, 3678 KiB  
Article
Detection and Prevention of False Data Injection Attacks in the Measurement Infrastructure of Smart Grids
by Muhammad Awais Shahid, Fiaz Ahmad, Fahad R. Albogamy, Ghulam Hafeez and Zahid Ullah
Sustainability 2022, 14(11), 6407; https://doi.org/10.3390/su14116407 - 24 May 2022
Cited by 8 | Viewed by 2463
Abstract
The smart grid has become a cyber-physical system and the more cyber it becomes, the more prone it is to cyber-attacks. One of the most important cyber-attacks in smart grids is false data injection (FDI) into its measurement infrastructure. This attack could manipulate [...] Read more.
The smart grid has become a cyber-physical system and the more cyber it becomes, the more prone it is to cyber-attacks. One of the most important cyber-attacks in smart grids is false data injection (FDI) into its measurement infrastructure. This attack could manipulate the control center in a way to execute wrong control actions on various generating units, causing system instabilities that could ultimately lead to power system blackouts. In this study, a novel false data detection and prevention paradigm was proposed for the measurement infrastructure in smart grids. Two techniques were devised to manage cyber-attacks, namely, the fixed dummy value model and the variable dummy value model. Limitations of the fixed dummy value model were identified and addressed in the variable dummy value model. Both methods were tested on an IEEE 14 bus system and it was shown through the results that an FDI attack that easily bypassed the bad data filter of the state estimator was successfully identified by the fixed dummy model. Second, attacks that were overlooked by the fixed dummy model were identified by the variable dummy method. In this way, the power system was protected from FDI attacks. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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25 pages, 1487 KiB  
Article
Effect of Crop Establishment Methods and Microbial Inoculations on Augmenting the Energy Efficiency and Nutritional Status of Rice and Wheat in Cropping System Mode
by Amit Anil Shahane, Yashbir Singh Shivay, Radha Prasanna, Dinesh Kumar and Ram Swaroop Bana
Sustainability 2022, 14(10), 5986; https://doi.org/10.3390/su14105986 - 15 May 2022
Cited by 1 | Viewed by 2076
Abstract
A field experiment was conducted for two consecutive years with the aim to quantify the role of different nutrient management variables such as microbial inoculation, zinc (Zn) fertilization and optimal and sub-optimal fertilization of nitrogen and phosphorus on the energetic and nutritional status [...] Read more.
A field experiment was conducted for two consecutive years with the aim to quantify the role of different nutrient management variables such as microbial inoculation, zinc (Zn) fertilization and optimal and sub-optimal fertilization of nitrogen and phosphorus on the energetic and nutritional status of the rice–wheat cropping system (RWCS). The said nutrient management variables were applied over six different crop establishment methods (CEMs) in RWCS viz. puddled transplanted rice (PTR), system of rice intensification (SRI) and aerobic rice system (ARS) in rice and conventional drill-sown wheat (CDW), system of wheat intensification (SWI) and zero-tillage wheat (ZTW) in wheat. Two microbial consortia viz. Anabaena sp. (CR1) + Providencia sp. (PR3) consortia (MC1) and Anabaena-Pseudomonas biofilmed formulations (MC2) were used in this study, while recommended dose of nitrogen (N) and phosphorus (P) (RDN) (120 kg N ha−1 and 25.8 kg P ha−1), 75% RDN and Zn fertilization (soil applied 5 kg Zn ha−1 through zinc sulphate heptahydrate) were the other variables. The contribution of microbial consortia, Zn fertilization and RDN (over 75% RDN) to net energy production of RWCS was 12.9–16.1 × 103 MJ ha−1, 10.1–11.0 × 103 MJ ha−1 and 11.7–15.3 × 103 MJ ha−1. Among the CEMs, the highest gross and net energy production was recorded in ARS–ZTW with lowest energy required for production of one tonne of system yield (2366–2523 MJ). The system protein yield varies from 494.1 to 957.7 kg ha−1 with highest protein yield in 75% RDN + MC2 + Zn applied ARS–ZTW. Among micronutrients, the uptake of Zn and iron (Fe) is sensitive to all studied variables, while manganese (Mn) and cupper (Cu) uptake was found significantly affected by CEMs alone. The combination of 75% RDN + MC2 + Zn in ARS–ZTW was found superior in all respects with 288.3 and 286.9 MJ ha−1 net energy production and 2320 and 2473 MJ energy required for production of one tonne system yield in the first and second year of study, respectively. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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16 pages, 1986 KiB  
Article
Deep-Learning-Based Adaptive Model for Solar Forecasting Using Clustering
by Sourav Malakar, Saptarsi Goswami, Bhaswati Ganguli, Amlan Chakrabarti, Sugata Sen Roy, K. Boopathi and A. G. Rangaraj
Energies 2022, 15(10), 3568; https://doi.org/10.3390/en15103568 - 13 May 2022
Cited by 4 | Viewed by 1476
Abstract
Accurate short-term solar forecasting is challenging due to weather uncertainties associated with cloud movements. Typically, a solar station comprises a single prediction model irrespective of time and cloud condition, which often results in suboptimal performance. In the proposed model, different categories of cloud [...] Read more.
Accurate short-term solar forecasting is challenging due to weather uncertainties associated with cloud movements. Typically, a solar station comprises a single prediction model irrespective of time and cloud condition, which often results in suboptimal performance. In the proposed model, different categories of cloud movement are discovered using K-medoid clustering. To ensure broader variation in cloud movements, neighboring stations were also used that were selected using a dynamic time warping (DTW)-based similarity score. Next, cluster-specific models were constructed. At the prediction time, the current weather condition is first matched with the different weather groups found through clustering, and a cluster-specific model is subsequently chosen. As a result, multiple models are dynamically used for a particular day and solar station, which improves performance over a single site-specific model. The proposed model achieved 19.74% and 59% less normalized root mean square error (NRMSE) and mean rank compared to the benchmarks, respectively, and was validated for nine solar stations across two regions and three climatic zones of India. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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14 pages, 2536 KiB  
Article
Analysis of the Integration of Drift Detection Methods in Learning Algorithms for Electrical Consumption Forecasting in Smart Buildings
by Deyslen Mariano-Hernández, Luis Hernández-Callejo, Martín Solís, Angel Zorita-Lamadrid, Oscar Duque-Pérez, Luis Gonzalez-Morales, Felix Santos García, Alvaro Jaramillo-Duque, Adalberto Ospino-Castro, Victor Alonso-Gómez and Hugo J. Bello
Sustainability 2022, 14(10), 5857; https://doi.org/10.3390/su14105857 - 12 May 2022
Cited by 7 | Viewed by 2038
Abstract
Buildings are currently among the largest consumers of electrical energy with considerable increases in CO2 emissions in recent years. Although there have been notable advances in energy efficiency, buildings still have great untapped savings potential. Within demand-side management, some tools have helped [...] Read more.
Buildings are currently among the largest consumers of electrical energy with considerable increases in CO2 emissions in recent years. Although there have been notable advances in energy efficiency, buildings still have great untapped savings potential. Within demand-side management, some tools have helped improve electricity consumption, such as energy forecast models. However, because most forecasting models are not focused on updating based on the changing nature of buildings, they do not help exploit the savings potential of buildings. Considering the aforementioned, the objective of this article is to analyze the integration of methods that can help forecasting models to better adapt to the changes that occur in the behavior of buildings, ensuring that these can be used as tools to enhance savings in buildings. For this study, active and passive change detection methods were considered to be integrators in the decision tree and deep learning models. The results show that constant retraining for the decision tree models, integrating change detection methods, helped them to better adapt to changes in the whole building’s electrical consumption. However, for deep learning models, this was not the case, as constant retraining with small volumes of data only worsened their performance. These results may lead to the option of using tree decision models in buildings where electricity consumption is constantly changing. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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17 pages, 9611 KiB  
Article
Exploratory Weather Data Analysis for Electricity Load Forecasting Using SVM and GRNN, Case Study in Bali, Indonesia
by Siti Aisyah, Arionmaro Asi Simaremare, Didit Adytia, Indra A. Aditya and Andry Alamsyah
Energies 2022, 15(10), 3566; https://doi.org/10.3390/en15103566 - 12 May 2022
Cited by 15 | Viewed by 3106
Abstract
Accurate forecasting of electricity load is essential for electricity companies, primarily for planning electricity generators. Overestimated or underestimated forecasting value may lead to inefficiency of electricity generator or electricity deficiency in the electricity grid system. Parameters that may affect electricity demand are the [...] Read more.
Accurate forecasting of electricity load is essential for electricity companies, primarily for planning electricity generators. Overestimated or underestimated forecasting value may lead to inefficiency of electricity generator or electricity deficiency in the electricity grid system. Parameters that may affect electricity demand are the weather conditions at the location of the electricity system. In this paper, we investigate possible weather parameters that affect electricity load. As a case study, we choose an area with an isolated electricity system, i.e., Bali Island, in Indonesia. We calculate correlations of various weather parameters with electricity load in Bali during the period 2018–2019. We use two machine learning models to design an electricity load forecasting system, i.e., the Generalized Regression Neural Network (GRNN) and Support Vector Machine (SVM), using features from various weather parameters. We design scenarios that add one-by-one weather parameters to investigate which weather parameters affect the electricity load. The results show that the weather parameter with the highest correlation value with the electricity load in Bali is the temperature, which is then followed by sun radiation and wind speed parameter. We obtain the best prediction with GRNN and SVR with a correlation coefficient value of 0.95 and 0.965, respectively. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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15 pages, 3759 KiB  
Article
A Novel Transfer Learning Method Based on Conditional Variational Generative Adversarial Networks for Fault Diagnosis of Wind Turbine Gearboxes under Variable Working Conditions
by Xiaobo Liu, Haifei Ma and Yibing Liu
Sustainability 2022, 14(9), 5441; https://doi.org/10.3390/su14095441 - 30 Apr 2022
Cited by 8 | Viewed by 1728
Abstract
The rapid development of artificial intelligence offers more opportunities for intelligent mechanical diagnosis. Recently, due to various reasons such as difficulty in obtaining fault data and random changes in operating conditions, deep transfer learning has achieved great attention in solving mechanical fault diagnoses. [...] Read more.
The rapid development of artificial intelligence offers more opportunities for intelligent mechanical diagnosis. Recently, due to various reasons such as difficulty in obtaining fault data and random changes in operating conditions, deep transfer learning has achieved great attention in solving mechanical fault diagnoses. In order to solve the problems of variable working conditions and data imbalance, a novel transfer learning method based on conditional variational generative adversarial networks (CVAE-GAN) is proposed to realize the fault diagnosis of wind turbine test bed data. Specifically, frequency spectra are employed as model signals, then the improved CVAE-GAN are implemented to generate missing data for other operating conditions. In order to reduce the difference in distribution between the source and target domains, the maximum mean difference (MMD) is used in the model to constrain the training of the target domain generation model. The generated data is used to supplement the missing sample data for fault classification. The verification results confirm that the proposed method is a promising tool that can obtain higher diagnosis efficiency. The feature embedding is visualized by t-distributed stochastic neighbor embedding (t-SNE) to test the effectiveness of the proposed model. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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15 pages, 2725 KiB  
Article
Comparison of Feedforward Perceptron Network with LSTM for Solar Cell Radiation Prediction
by Tugba Ozdemir, Fatma Taher, Babajide O. Ayinde, Jacek M. Zurada and Ozge Tuzun Ozmen
Appl. Sci. 2022, 12(9), 4463; https://doi.org/10.3390/app12094463 - 28 Apr 2022
Cited by 5 | Viewed by 1760
Abstract
Intermittency of electrical power in developing countries, as well as some European countries such as Turkey, can be eluded by taking advantage of solar energy. Correct prediction of solar radiation constitutes a very important step to take advantage of PV solar panels. We [...] Read more.
Intermittency of electrical power in developing countries, as well as some European countries such as Turkey, can be eluded by taking advantage of solar energy. Correct prediction of solar radiation constitutes a very important step to take advantage of PV solar panels. We propose an experimental study to predict the amount of solar radiation using a classical artificial neural network (ANN) and deep learning methods. PV panel and solar radiation data were collected at Duzce University in Turkey. Moreover, we included meteorological data collected from the Meteorological Ministry of Turkey in Duzce. Data were collected on a daily basis with a 5-min interval. Data were cleaned and preprocessed to train long-short-term memory (LSTM) and ANN models to predict the solar radiation amount of one day ahead. Models were evaluated using coefficient of determination (R2), mean square error (MSE), root mean squared error (RMSE), mean absolute error (MAE), and mean biased error (MBE). LSTM outperformed ANN with R2, MSE, RMSE, MAE, and MBE of 0.93, 0.008, 0.089, 0.17, and 0.09, respectively. Moreover, we compared our results with two similar studies in the literature. The proposed study paves the way for utilizing renewable energy by leveraging the usage of PV panels. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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27 pages, 6458 KiB  
Article
Metaheuristic Optimization-Based Path Planning and Tracking of Quadcopter for Payload Hold-Release Mission
by Egemen Belge, Aytaç Altan and Rıfat Hacıoğlu
Electronics 2022, 11(8), 1208; https://doi.org/10.3390/electronics11081208 - 11 Apr 2022
Cited by 73 | Viewed by 3531
Abstract
Under harsh geographical conditions where manned flight is not possible, the ability of the unmanned aerial vehicle (UAV) to successfully carry out the payload hold–release mission by avoiding obstacles depends on the optimal path planning and tracking performance of the UAV. The ability [...] Read more.
Under harsh geographical conditions where manned flight is not possible, the ability of the unmanned aerial vehicle (UAV) to successfully carry out the payload hold–release mission by avoiding obstacles depends on the optimal path planning and tracking performance of the UAV. The ability of the UAV to plan and track the path with minimum energy and time consumption is possible by using the flight parameters. This study performs the optimum path planning and tracking using Harris hawk optimization (HHO)–grey wolf optimization (GWO), a hybrid metaheuristic optimization algorithm, to enable the UAV to actualize the payload hold–release mission avoiding obstacles. In the study, the hybrid HHO–GWO algorithm, which stands out with its avoidance of local minima and speed convergence, is used to successfully obtain the feasible and effective path. In addition, the effect of the mass change uncertainty of the UAV on optimal path planning and tracking performance is determined. The effectiveness of the proposed approach is tested by comparing it with the metaheuristic swarm optimization algorithms such as particle swarm optimization (PSO) and GWO. The experimental results obtained indicate that the proposed algorithm generates a fast and safe optimal path without becoming stuck with local minima, and the quadcopter tracks the generated path with minimum energy and time consumption. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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28 pages, 6070 KiB  
Article
Development of a Predicting Model for Calculating the Geometry and the Characteristic Curves of Pumps Running as Turbines in Both Operating Modes
by Silvio Barbarelli, Vincenzo Pisano and Mario Amelio
Energies 2022, 15(7), 2669; https://doi.org/10.3390/en15072669 - 6 Apr 2022
Cited by 6 | Viewed by 2127
Abstract
This article is part of a scientific research project dedicated to the study of plants generating electricity from hydraulic sources by exploiting the technology of inverted flow centrifugal pumps, also known as PAT. The main purpose is to provide a contribution to the [...] Read more.
This article is part of a scientific research project dedicated to the study of plants generating electricity from hydraulic sources by exploiting the technology of inverted flow centrifugal pumps, also known as PAT. The main purpose is to provide a contribution to the methodologies already existing in the literature, creating a one-dimensional model capable of predicting the characteristic curves of the machine, in both operating modes, without knowing its geometry. The first part of the work is therefore focused on the description of the fluid dynamic model, capable of determining the losses in the various sections of the machine, using different calculation approaches. The development of this model was carried out using a set of six centrifugal pumps, measured at the DIMEG Department of the University of Calabria and at the University of Trento. For this range of pumps, the characteristic curves were therefore obtained, both in pump and turbine operation. The second part of this work focuses on the description of the geometric model, useful as generally few data are provided in the manufacturer’s catalog, which is necessary for the correct installation of the machine. The geometric model can determine, using these parameters and through good design techniques and statistical diagrams, the entire geometry of the machine. This model refers to a pump prototype, having a simplified geometry, for which the characteristic curves of the PAT are obtained in pump operation. These curves are compared with those present in the manufacturer’s catalog, and if they show too high deviations, it is possible to act on some geometric parameters, chosen based on a sensitivity analysis. Once satisfactory results have been obtained, it is possible to obtain the characteristic curves also in turbine operation. This procedure has been finally applied to another PAT, taken as an example. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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20 pages, 2026 KiB  
Article
Neural Ordinary Differential Equations for Grey-Box Modelling of Lithium-Ion Batteries on the Basis of an Equivalent Circuit Model
by Jennifer Brucker, René Behmann, Wolfgang G. Bessler and Rainer Gasper
Energies 2022, 15(7), 2661; https://doi.org/10.3390/en15072661 - 5 Apr 2022
Cited by 2 | Viewed by 2103
Abstract
Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. [...] Read more.
Lithium-ion batteries exhibit a dynamic voltage behaviour depending nonlinearly on current and state of charge. The modelling of lithium-ion batteries is therefore complicated and model parametrisation is often time demanding. Grey-box models combine physical and data-driven modelling to benefit from their respective advantages. Neural ordinary differential equations (NODEs) offer new possibilities for grey-box modelling. Differential equations given by physical laws and NODEs can be combined in a single modelling framework. Here we demonstrate the use of NODEs for grey-box modelling of lithium-ion batteries. A simple equivalent circuit model serves as a basis and represents the physical part of the model. The voltage drop over the resistor–capacitor circuit, including its dependency on current and state of charge, is implemented as a NODE. After training, the grey-box model shows good agreement with experimental full-cycle data and pulse tests on a lithium iron phosphate cell. We test the model against two dynamic load profiles: one consisting of half cycles and one dynamic load profile representing a home-storage system. The dynamic response of the battery is well captured by the model. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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18 pages, 3056 KiB  
Article
Forecasting the Total South African Unplanned Capability Loss Factor Using an Ensemble of Deep Learning Techniques
by Sibonelo Motepe, Ali N. Hasan and Thokozani Shongwe
Energies 2022, 15(7), 2546; https://doi.org/10.3390/en15072546 - 31 Mar 2022
Cited by 3 | Viewed by 1379
Abstract
Unplanned power plant failures have been seen to be a major cause of power shortages, and thus customer power cuts, in the South African power grid. These failures are measured as the unplanned capability loss factor (UCLF). The study of South Africa’s UCLF [...] Read more.
Unplanned power plant failures have been seen to be a major cause of power shortages, and thus customer power cuts, in the South African power grid. These failures are measured as the unplanned capability loss factor (UCLF). The study of South Africa’s UCLF is almost non-existent. Parameters that affect the future UCLF are, thus, still not well understood, making it challenging to forecast when power shortages may be experienced. This paper presents a novel study of South African UCLF forecasting using state-of-the-art deep learning techniques. The study further introduces a novel deep learning ensemble South African UCLF forecasting system. The performance of three of the best recent forecasting techniques, namely, long short-term memory recurrent neural network (LSTM-RNN), deep belief network (DBN), and optimally pruned extreme learning machines (OP-ELM), as well as their aggregated ensembles, are investigated for South African UCLF forecasting. The impact of three key parameters (installed capacity, demand, and planned capability loss factor) on the future UCLF is investigated. The results showed that the exclusion of installed capacity in the LSTM-RNN, DBN, OP-ELM, and ensemble models doubled the UCLF forecasting error. It was also found that an ensemble model of two LSTM-RNN models achieved the lowest errors with a symmetric mean absolute percentage error (sMAPE) of 6.43%, mean absolute error (MAE) of 7.36%, and root-mean-square error (RMSE) of 9.21%. LSTM-RNN also achieved the lowest errors amongst the individual models. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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20 pages, 5825 KiB  
Article
Machine Learning Algorithms for Flow Pattern Classification in Pulsating Heat Pipes
by Jose Loyola-Fuentes, Luca Pietrasanta, Marco Marengo and Francesco Coletti
Energies 2022, 15(6), 1970; https://doi.org/10.3390/en15061970 - 8 Mar 2022
Cited by 10 | Viewed by 2577
Abstract
Owing to their simple construction, cost effectiveness, and high thermal efficiency, pulsating heat pipes (PHPs) are growing in popularity as cooling devices for electronic equipment. While PHPs can be very resilient as passive cooling systems, their operation relies on the establishment and persistence [...] Read more.
Owing to their simple construction, cost effectiveness, and high thermal efficiency, pulsating heat pipes (PHPs) are growing in popularity as cooling devices for electronic equipment. While PHPs can be very resilient as passive cooling systems, their operation relies on the establishment and persistence of slug/plug flow as the dominant flow regime. It is, therefore, paramount to predict the flow regime accurately as a function of various operating parameters and design geometry. Flow pattern maps that capture flow regimes as a function of nondimensional numbers (e.g., Froude, Weber, and Bond numbers) have been proposed in the literature. However, the prediction of flow patterns based on deterministic models is a challenging task that relies on the ability of explaining the very complex underlying phenomena or the ability to measure parameters, such as the bubble acceleration, which are very difficult to know beforehand. In contrast, machine learning algorithms require limited a priori knowledge of the system and offer an alternative approach for classifying flow regimes. In this work, experimental data collected for two working fluids (ethanol and FC-72) in a PHP at different gravity and power input levels, were used to train three different classification algorithms (namely K-nearest neighbors, random forest, and multilayer perceptron). The data were previously labeled via visual classification using the experimental results. A comparison of the resulting classification accuracy was carried out via confusion matrices and calculation of accuracy scores. The algorithm presenting the highest classification performance was selected for the development of a flow pattern map, which accurately indicated the flow pattern transition boundaries between slug/plug and annular flows. Results indicate that, once experimental data are available, the proposed machine learning approach could help in reducing the uncertainty in the classification of flow patterns and improve the predictions of the flow regimes. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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10 pages, 1919 KiB  
Article
An Unsupervised Mutual Information Feature Selection Method Based on SVM for Main Transformer Condition Diagnosis in Nuclear Power Plants
by Wenmin Yu, Ren Yu and Jun Tao
Sustainability 2022, 14(5), 2700; https://doi.org/10.3390/su14052700 - 25 Feb 2022
Cited by 1 | Viewed by 1327
Abstract
Dissolved gas in oil (DGA) is a common means of monitoring the condition of an oil-immersed transformer. The concentration of dissolved gas and the ratio of different gases are important indexes to judge the condition of power transformers. Monitoring devices for dissolved gas [...] Read more.
Dissolved gas in oil (DGA) is a common means of monitoring the condition of an oil-immersed transformer. The concentration of dissolved gas and the ratio of different gases are important indexes to judge the condition of power transformers. Monitoring devices for dissolved gas in oil are widely installed in main transformers, but there are few recorded fault data of main transformers. The special operation and maintenance modes of main transformers leads to the fault modes particularity of main transformers. In order to solve the problem of insufficient samples and the feature uncertainty, this paper puts forward an unsupervised mutual information method to select the feature verified by the optimized support vector machine (SVM) model of particle swarm optimization (PSO) method and tries to find the feature sequence with better performance. The methos is validated by data from nuclear power transformers. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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26 pages, 819 KiB  
Article
Performance Analysis of Machine Learning Algorithms for Energy Demand–Supply Prediction in Smart Grids
by Eric Cebekhulu, Adeiza James Onumanyi and Sherrin John Isaac
Sustainability 2022, 14(5), 2546; https://doi.org/10.3390/su14052546 - 22 Feb 2022
Cited by 19 | Viewed by 2828
Abstract
The use of machine learning (ML) algorithms for power demand and supply prediction is becoming increasingly popular in smart grid systems. Due to the fact that there exist many simple ML algorithms/models in the literature, the question arises as to whether there is [...] Read more.
The use of machine learning (ML) algorithms for power demand and supply prediction is becoming increasingly popular in smart grid systems. Due to the fact that there exist many simple ML algorithms/models in the literature, the question arises as to whether there is any significant advantage(s) among these different ML algorithms, particularly as it pertains to power demand/supply prediction use cases. Toward answering this question, we examined six well-known ML algorithms for power prediction in smart grid systems, including the artificial neural network, Gaussian regression (GR), k-nearest neighbor, linear regression, random forest, and support vector machine (SVM). First, fairness was ensured by undertaking a thorough hyperparameter tuning exercise of the models under consideration. As a second step, power demand and supply statistics from the Eskom database were selected for day-ahead forecasting purposes. These datasets were based on system hourly demand as well as renewable generation sources. Hence, when their hyperparameters were properly tuned, the results obtained within the boundaries of the datasets utilized showed that there was little/no significant difference in the quantitative and qualitative performance of the different ML algorithms. As compared to photovoltaic (PV) power generation, we observed that these algorithms performed poorly in predicting wind power output. This could be related to the unpredictable wind-generated power obtained within the time range of the datasets employed. Furthermore, while the SVM algorithm achieved the slightly quickest empirical processing time, statistical tests revealed that there was no significant difference in the timing performance of the various algorithms, except for the GR algorithm. As a result, our preliminary findings suggest that using a variety of existing ML algorithms for power demand/supply prediction may not always yield statistically significant comparative prediction results, particularly for sources with regular patterns, such as solar PV or daily consumption rates, provided that the hyperparameters of such algorithms are properly fine tuned. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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20 pages, 404 KiB  
Article
Generalized Sliding Mode Observers for Simultaneous Fault Reconstruction in the Presence of Uncertainty and Disturbance
by Ashkan Taherkhani, Farhad Bayat, Kaveh Hooshmandi and Andrzej Bartoszewicz
Energies 2022, 15(4), 1411; https://doi.org/10.3390/en15041411 - 15 Feb 2022
Viewed by 1331
Abstract
In this paper, a generalized sliding mode observer design method is proposed for the robust reconstruction of sensors and actuators faults in the presence of both unknown disturbances and uncertainties. For this purpose, the effect of uncertainty and disturbance on the system has [...] Read more.
In this paper, a generalized sliding mode observer design method is proposed for the robust reconstruction of sensors and actuators faults in the presence of both unknown disturbances and uncertainties. For this purpose, the effect of uncertainty and disturbance on the system has been considered in generalized state-space form, and the LMI tool is combined with the concept of an equivalent output error injection method to reduce the effects of them on the reconstruction process. The upper bound of the disturbance and uncertainty are minimized in the design of the sliding motion so that the reconstruction of the faults will be minimized. The design method is applied for actuator faults in the generalized state-space form, and then with some suitable filtering, the method extends as sensors and actuators coincidentally faults. Since in the proposed approach, the state trajectories do not leave the sliding manifold even in simultaneous sensors and actuators faults, then the faults are reconstructed based upon information retrieved from the equivalent output error injection signal. Due to the importance of the robust fault reconstruction in the wind energy conversion system (WECS), the proposed approach is successfully applied to a 5 MW wind turbine system. The simulation results verify the robust performances of the proposed approach in the presence of unknown perturbations and uncertainties. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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18 pages, 7489 KiB  
Article
Electricity Pattern Analysis by Clustering Domestic Load Profiles Using Discrete Wavelet Transform
by Senfeng Cen, Jae Hung Yoo and Chang Gyoon Lim
Energies 2022, 15(4), 1350; https://doi.org/10.3390/en15041350 - 13 Feb 2022
Cited by 17 | Viewed by 2685
Abstract
Energy demand has grown explosively in recent years, leading to increased attention of energy efficiency (EE) research. Demand response (DR) programs were designed to help power management entities meet energy balance and change end-user electricity usage. Advanced real-time meters (RTM) collect a large [...] Read more.
Energy demand has grown explosively in recent years, leading to increased attention of energy efficiency (EE) research. Demand response (DR) programs were designed to help power management entities meet energy balance and change end-user electricity usage. Advanced real-time meters (RTM) collect a large amount of fine-granular electric consumption data, which contain valuable information. Understanding the energy consumption patterns for different end users can support demand side management (DSM). This study proposed clustering algorithms to segment consumers and obtain the representative load patterns based on diurnal load profiles. First, the proposed method uses discrete wavelet transform (DWT) to extract features from daily electricity consumption data. Second, the extracted features are reconstructed using a statistical method, combined with Pearson’s correlation coefficient and principal component analysis (PCA) for dimensionality reduction. Lastly, three clustering algorithms are employed to segment daily load curves and select the most appropriate algorithm. We experimented our method on the Manhattan dataset and the results indicated that clustering algorithms, combined with discrete wavelet transform, improve the clustering performance. Additionally, we discussed the clustering result and load pattern analysis of the dataset with respect to the electricity pattern. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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24 pages, 3219 KiB  
Article
Intelligent Reasoning Rules for Home Energy Management (IRRHEM): Algeria Case Study
by Djamel Saba, Omar Cheikhrouhou, Wajdi Alhakami, Youcef Sahli, Abdelkader Hadidi and Habib Hamam
Appl. Sci. 2022, 12(4), 1861; https://doi.org/10.3390/app12041861 - 11 Feb 2022
Cited by 7 | Viewed by 1690
Abstract
Algeria is characterized by extreme cold in winter and high heat and humidity in summer. This leads to an increase in the use of electrical appliances, which has a negative impact on electrical energy consumption and its high costs, especially with the high [...] Read more.
Algeria is characterized by extreme cold in winter and high heat and humidity in summer. This leads to an increase in the use of electrical appliances, which has a negative impact on electrical energy consumption and its high costs, especially with the high price of electricity in Algeria. In this context, artificial intelligence can help to regulate the daily consumption of electricity, by optimizing the exploitation of natural resources and alerting the individual to avoid energy wasting. This paper proposes a decision-making tool (IRRHEM) for managing electrical energy at smart home. The IRRHEM solution is based on three elements: the use of natural resources, the notification of the inhabitants in case of resources misuse or wasting behavior, and the aggregation of similar activities at same time. Additionally, based on the proposed intelligent reasoning rules, residents’ behavior and activities are represented by OWL (Ontology Web Language) and written and executed through SWRL (Semantic Web Rule Language). Finally, the (IRRHEM) solution is tested in a home located in Algiers city inhabited by a family of four persons. The IRRHEM performance evaluation results are very promising and show a 3.60% rate of energy saving. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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14 pages, 14707 KiB  
Article
Learning-Aided Optimal Power Flow Based Fast Total Transfer Capability Calculation
by Ji’ang Liu, Youbo Liu, Gao Qiu and Xiao Shao
Energies 2022, 15(4), 1320; https://doi.org/10.3390/en15041320 - 11 Feb 2022
Cited by 5 | Viewed by 1136
Abstract
Total transfer capability (TTC) is a vital security indicator for power exchange among areas. It characterizes time-variants and transient stability dynamics, and thus is challenging to evaluate efficiently, which can jeopardize operational safety. A leaning-aided optimal power flow method is proposed to handle [...] Read more.
Total transfer capability (TTC) is a vital security indicator for power exchange among areas. It characterizes time-variants and transient stability dynamics, and thus is challenging to evaluate efficiently, which can jeopardize operational safety. A leaning-aided optimal power flow method is proposed to handle the above challenges. At the outset, deep learning (DL) is utilized to globally establish real-time transient stability estimators in parametric space, such that the dimensionality of dynamic simulators can be reduced. The computationally intensive transient stability constraints in TTC calculation and their sensitivities are therewith converted into fast forward and backward processes. The DL-aided constrained model is finally solved by nonlinear programming. The numerical results on the modified IEEE 39-bus system demonstrate that the proposed method outperforms several model-based methods in accuracy and efficiency. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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16 pages, 3172 KiB  
Article
Classification of Ground-Based Cloud Images by Improved Combined Convolutional Network
by Wen Zhu, Tianliang Chen, Beiping Hou, Chen Bian, Aihua Yu, Lingchao Chen, Ming Tang and Yuzhen Zhu
Appl. Sci. 2022, 12(3), 1570; https://doi.org/10.3390/app12031570 - 1 Feb 2022
Cited by 6 | Viewed by 2156
Abstract
Changes in clouds can affect the outpower of photovoltaics (PVs). Ground-based cloud images classification is an important prerequisite for PV power prediction. Due to the intra-class difference and inter-class similarity of cloud images, the classical convolutional network is obviously insufficient in distinguishing ability. [...] Read more.
Changes in clouds can affect the outpower of photovoltaics (PVs). Ground-based cloud images classification is an important prerequisite for PV power prediction. Due to the intra-class difference and inter-class similarity of cloud images, the classical convolutional network is obviously insufficient in distinguishing ability. In this paper, a classification method of ground-based cloud images by improved combined convolutional network is proposed. To solve the problem of sub-network overfitting caused by redundancy of pixel information, overlap pooling kernel is used to enhance the elimination effect of information redundancy in the pooling layer. A new channel attention module, ECA-WS (Efficient Channel Attention–Weight Sharing), is introduced to improve the network’s ability to express channel information. The decision fusion algorithm is employed to fuse the outputs of sub-networks with multi-scales. According to the number of cloud images in each category, different weights are applied to the fusion results, which solves the problem of network scale limitation and dataset imbalance. Experiments are carried out on the open MGCD dataset and the self-built NRELCD dataset. The results show that the proposed model has significantly improved the classification accuracy compared with the classical network and the latest algorithms. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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24 pages, 5662 KiB  
Article
0D Dynamic Modeling and Experimental Characterization of a Biomass Boiler with Mass and Energy Balance
by Fateh Mameri, Eric Delacourt, Céline Morin and Jesse Schiffler
Entropy 2022, 24(2), 202; https://doi.org/10.3390/e24020202 - 28 Jan 2022
Viewed by 3436
Abstract
The paper presents an experimental study and a 0D dynamic modeling of a biomass boiler based on the Bond Graph formalism from mass and energy balance. The biomass boiler investigated in this study is an automatic pellet boiler with a nominal power of [...] Read more.
The paper presents an experimental study and a 0D dynamic modeling of a biomass boiler based on the Bond Graph formalism from mass and energy balance. The biomass boiler investigated in this study is an automatic pellet boiler with a nominal power of 30 kW with a fixed bed. The balances allow to model as time function the flue gas enthalpy flux variation and the thermal transfers between the flue gas and the walls of the boiler subsystems. The main objective is to build a model to represent the dynamic thermal behavior of the boiler. Indeed, small domestic boilers have discontinuous operating phases when the set temperature is reached. The global thermal transfer coefficients for the boiler subsystems are obtained according to an iterative calculation by inverse method. The boiler has an average efficiency of 67.5% under our operating conditions and the radiation is the dominant thermal transfer by reaching 97.6% of the total thermal transfers inside the combustion chamber. The understanding of the dynamic behavior of the boiler during the operating phases allows to evaluate its energy performances. The proposed model is both stimulated and validated using experimental results carried out on the boiler. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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12 pages, 35681 KiB  
Article
An Ultra-Low Power Threshold Voltage Variable Artificial Retina Neuron
by Qiguang Wang, Guangchen Pan and Yanfeng Jiang
Electronics 2022, 11(3), 365; https://doi.org/10.3390/electronics11030365 - 25 Jan 2022
Cited by 1 | Viewed by 2897
Abstract
An artificial retina neuron is proposed and implemented by CMOS technology. It can be used as an image sensor in the Artificial Intelligence (AI) field with the benefit of ultra-low power consumption. The artificial neuron can generate signals in spike shape with pre-designed [...] Read more.
An artificial retina neuron is proposed and implemented by CMOS technology. It can be used as an image sensor in the Artificial Intelligence (AI) field with the benefit of ultra-low power consumption. The artificial neuron can generate signals in spike shape with pre-designed frequencies under different light intensities. The power consumption is reduced by removing the film capacitor. The comparator is adopted to improve the stability of the circuit, and the power consumption of the comparator is optimized. The power consumption of the proposed CMOS neuron circuit is suppressed. The ultra-low-power artificial neuron with variable threshold shows a frequency range of 0.8–80 kHz when the input current is varied from 1 pA to 150 pA. The minimum DC power is 35 pW when the input current is 5 pA. The minimum energy of the neuron is 3 fJ. The proposed ultra-low-power artificial retina neuron has wide potential applications in the field of AI. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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17 pages, 3431 KiB  
Article
Prediction of Gas Concentration Based on LSTM-LightGBM Variable Weight Combination Model
by Xiangqian Wang, Ningke Xu, Xiangrui Meng and Haoqian Chang
Energies 2022, 15(3), 827; https://doi.org/10.3390/en15030827 - 24 Jan 2022
Cited by 13 | Viewed by 3016
Abstract
Gas accidents threaten the safety of underground coal mining, which are always accompanied by abnormal gas concentration trend. The purpose of this paper is to improve the prediction accuracy of gas concentration so as to prevent gas accidents and improve the level of [...] Read more.
Gas accidents threaten the safety of underground coal mining, which are always accompanied by abnormal gas concentration trend. The purpose of this paper is to improve the prediction accuracy of gas concentration so as to prevent gas accidents and improve the level of coal mine safety management. Combining the LSTM model with the LightGBM model, the LSTM-LightGBM model is proposed with variable weight combination method based on residual assignment, which considers not only the time subsequence feature of data, but also the nonlinear characteristics of data. During the data preprocessing, the optimal parameters of gas concentration prediction are determined through the analysis of the Pearson correlation coefficients of different sensor data. The experimental results demonstrate that the mean absolute errors of LSTM-LighGBM, LSTM and LightGBM are 1.94%, 2.19% and 2.77%, respectively. The accuracy of LSTM-LightGBM variable weight combination model is better than that of the two above models, respectively. In this way, this study provides a novel idea and method for gas accident prevention based on gas concentration prediction. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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13 pages, 2752 KiB  
Article
Machine Learning Approach for Maximizing Thermoelectric Properties of BiCuSeO and Discovering New Doping Element
by Nuttawat Parse, Chakrit Pongkitivanichkul and Supree Pinitsoontorn
Energies 2022, 15(3), 779; https://doi.org/10.3390/en15030779 - 21 Jan 2022
Cited by 6 | Viewed by 2456
Abstract
Machine learning (ML) has increasingly received interest as a new approach to accelerating development in materials science. It has been applied to thermoelectric materials research for discovering new materials and designing experiments. Generally, the amount of data in thermoelectric materials research, especially experimental [...] Read more.
Machine learning (ML) has increasingly received interest as a new approach to accelerating development in materials science. It has been applied to thermoelectric materials research for discovering new materials and designing experiments. Generally, the amount of data in thermoelectric materials research, especially experimental data, is very small leading to an undesirable ML model. In this work, the ML model for predicting ZT of the doped BiCuSeO was implemented. The method to improve the model was presented step-by-step. This included normalizing the experimental ZT of the doped BiCuSeO with the pristine BiCuSeO, selecting data for the BiCuSeO doped at Bi-site only, and limiting important features for the model construction. The modified model showed significant improvement, with the R2 of 0.93, compared to the original model (R2 of 0.57). The model was validated and used to predict the ZT of the unknown doped BiCuSeO compounds. The predicted result was logically justified based on the thermoelectric principle. It means that the ML model can guide the experiments to improve the thermoelectric properties of BiCuSeO and can be extended to other materials. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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12 pages, 510 KiB  
Article
Sources and Sectoral Trend Analysis of CO2 Emissions Data in Nigeria Using a Modified Mann-Kendall and Change Point Detection Approaches
by Ogundele Lasun Tunde, Okunlola Oluyemi Adewole, Mohannad Alobid, István Szűcs and Yacouba Kassouri
Energies 2022, 15(3), 766; https://doi.org/10.3390/en15030766 - 21 Jan 2022
Cited by 9 | Viewed by 2116
Abstract
In Nigeria, the high dependence on fossil fuels for energy generation and utilization in various sectors of the economy has resulted in the emission of a large quantity of carbon dioxide (CO2), which is one of the criteria gaseous pollutants that [...] Read more.
In Nigeria, the high dependence on fossil fuels for energy generation and utilization in various sectors of the economy has resulted in the emission of a large quantity of carbon dioxide (CO2), which is one of the criteria gaseous pollutants that is frequently encountered in the environment. The high quantity of CO2 has adverse implications on human health and serious damaging effects on the environment. In this study, multi-decade (1971–2014) CO2-emissions data for Nigeria were obtained from the World Development Indicator (WDI). The data were disaggregated into various emission sources: gaseous fuel consumption (GFC), liquid fuel consumption (LFC), solid fuel consumption (SFC), transport (TRA), electricity and heat production (EHP), residential buildings and commercial and public services (RSCPS), manufacturing industries and construction (MINC), and other sectors excluding residential buildings and commercial and public services (OSEC). The analysis was conducted for a sectorial trend using a rank-based non-parametric modified Mann–Kendall (MK) statistical approach and a change point detection method. The results showed that the CO2 emissions from TRA were significantly high, followed by LFC. The GFC, LFC, EHP, and OSEC had a positive Sen’s slope, while SFC, TRA, and MINC had a negative Sen’s slope. The trend analysis indicated multiple changes for TRA and OSEC, while other sources had a change point at a particular year. These results are useful for knowledge of CO2-emission sources in Nigeria and for future understanding of the trend of its emission for proper environmental planning. The severe effects of CO2 on the atmospheric environment of Nigeria may be worsened in the future due to some major sources such as transportation services and electricity generation that are inevitable for enviable standard of living in an urban setting. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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18 pages, 6147 KiB  
Article
Wind Speed Prediction for Offshore Sites Using a Clockwork Recurrent Network
by Yuxuan Shi, Yanyu Wang and Haoran Zheng
Energies 2022, 15(3), 751; https://doi.org/10.3390/en15030751 - 20 Jan 2022
Cited by 5 | Viewed by 1834
Abstract
Offshore sites show greater potential for wind energy utilization than most onshore sites. When planning an offshore wind power farm, the speed of offshore wind is used to estimate various operation parameters, such as the power output, extreme wind load, and fatigue load. [...] Read more.
Offshore sites show greater potential for wind energy utilization than most onshore sites. When planning an offshore wind power farm, the speed of offshore wind is used to estimate various operation parameters, such as the power output, extreme wind load, and fatigue load. Accurate speed prediction is crucial to the running of wind power farms and the security of smart grids. Unlike onshore wind, offshore wind has the characteristics of random, intermittent, and chaotic, which will cause the time series of wind speeds to have strong nonlinearity. It will bring greater difficulties to offshore wind speed predictions, which traditional recurrent neural networks cannot deal with for lacking in long-term dependency. An offshore wind speed prediction method is proposed by using a clockwork recurrent network (CWRNN). In a CWRNN model, the hidden layer is subdivided into several parts and each part is allocated a different clock speed. Under the mechanism, the long-term dependency of the recurrent neural network can be easily addressed, which can furthermore effectively solve the problem of strong nonlinearity in offshore speed winds. The experiments are performed by using the actual data of two different offshore sites located in the Caribbean Sea and one onshore site located in the interior of the United States, to verify the performance of the model. The results show that the prediction model achieves significant accuracy improvement. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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24 pages, 8745 KiB  
Article
Misfire Detection Using Crank Speed and Long Short-Term Memory Recurrent Neural Network
by Xinwei Wang, Pan Zhang, Wenzhi Gao, Yong Li, Yanjun Wang and Haoqian Pang
Energies 2022, 15(1), 300; https://doi.org/10.3390/en15010300 - 3 Jan 2022
Cited by 6 | Viewed by 2159
Abstract
In this work, a new approach was developed for the detection of engine misfire based on the long short-term memory recurrent neural network (LSTM RNN) using crank speed signal. The datasets are acquired from a six-cylinder-inline, turbo-charged diesel engine. Previous works investigated misfire [...] Read more.
In this work, a new approach was developed for the detection of engine misfire based on the long short-term memory recurrent neural network (LSTM RNN) using crank speed signal. The datasets are acquired from a six-cylinder-inline, turbo-charged diesel engine. Previous works investigated misfire detection in a limited range of engine running speed, running load or misfire types. In this work, the misfire patterns consist of normal condition, six types of one-cylinder misfire faults and fifteen types of two-cylinder misfire faults. All the misfire patterns are tested under wide range of running conditions of the tested engine. The traditional misfire detection method is tested on the datasets first, and the result show its limitation on high-speed low-load conditions. The LSTM RNN is a type of artificial neural network which has the ability of considering both the current input in-formation and the previous input information; hence it is helpful in extracting features of crank speed in which the misfire-induced speed fluctuation will last one or a few cycles. In order to select the engine operating conditions for network training properly, five data division strategies are attempted. For the sake of acquiring high performance of designed network, four types of network structure are tested. The results show that, utilizing the datasets in this work, the LSTM RNN based algorithm can overcome the limitation at high-speed low-load conditions of traditional misfire detection method. Moreover, the network which takes fixed segment of raw speed signal as input and takes misfire or fault-free labels as output achieves the best performance with the misfire diagnosis accuracy not less than 99.90%. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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17 pages, 3054 KiB  
Article
The Efficiency of Drones Usage for Safety and Rescue Operations in an Open Area: A Case from Poland
by Norbert Tuśnio and Wojciech Wróblewski
Sustainability 2022, 14(1), 327; https://doi.org/10.3390/su14010327 - 29 Dec 2021
Cited by 13 | Viewed by 5269
Abstract
The use of unmanned aerial systems (UAS) is becoming increasingly frequent during search and rescue (SAR) operations conducted to find missing persons. These systems have proven to be particularly useful for operations executed in the wilderness, i.e., in open and mountainous areas. The [...] Read more.
The use of unmanned aerial systems (UAS) is becoming increasingly frequent during search and rescue (SAR) operations conducted to find missing persons. These systems have proven to be particularly useful for operations executed in the wilderness, i.e., in open and mountainous areas. The successful implementation of those systems is possible thanks to the potential offered by unmanned aerial vehicles (UAVs), which help achieve a considerable reduction in operational times and consequently allow a much quicker finding of lost persons. This is crucial to enhance their chances of survival in extreme conditions (withholding hydration, food and medicine, and hypothermia). The paper presents the results of a preliminary assessment of a search and rescue method conducted in an unknown terrain, where groups were coordinated with the use of UAVs and a ground control station (GCS) workstation. The conducted analysis was focused on assessing conditions that would help minimise the time of arrival of the rescue team to the target, which in real conditions could be a missing person identified on aerial images. The results of executed field tests have proven that the time necessary to reach injured persons can be substantially shortened if imaging recorded by UAV is deployed, as it considerably enhances the chance of survival in an emergency situation. The GCS workstation is also one of the crucial components in the search system, which assures image transmission from the UAV to participants of the search operation and radio signal amplification in a difficult terrain. The effectiveness of the search system was tested by comparing the arrival times of teams equipped with GPS and a compass and those not equipped with such equipment. The article also outlined the possibilities of extending the functionality of the search system with the SARUAV module, which was used to find a missing person in Poland. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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15 pages, 3797 KiB  
Article
A Spectrum Correction Method Based on Optimizing Turbulence Intensity
by Wenwu Yi, Ziqi Lu, Junbo Hao, Xinge Zhang, Yan Chen and Zhihong Huang
Appl. Sci. 2022, 12(1), 66; https://doi.org/10.3390/app12010066 - 22 Dec 2021
Cited by 3 | Viewed by 2365
Abstract
Based on the classical spectral representation method of simulating turbulent wind speed fluctuation, a harmonic superposition algorithm was introduced in detail to calculate the homogeneous turbulence wind field simulation in space. From the view of the validity of the numerical simulation results in [...] Read more.
Based on the classical spectral representation method of simulating turbulent wind speed fluctuation, a harmonic superposition algorithm was introduced in detail to calculate the homogeneous turbulence wind field simulation in space. From the view of the validity of the numerical simulation results in MATLAB and the simulation efficiency, this paper discussed the reason for the bias existing between three types of turbulence intensity involved in the whole simulation process: simulated turbulence intensity, setting reference turbulence intensity, and theoretical turbulence intensity. Therefore, a novel spectral correction method of a standard deviation compensation coefficient was proposed. The simulation verification of the correction method was carried out based on the Kaimal spectrum recommended by IEC61400-1 by simulating the uniform turbulent wind field in one-dimensional space at the height of the hub of a 15 MW wind turbine and in two-dimensional space in the rotor swept area. The results showed that the spectral correction method proposed in this paper can effectively optimize the turbulence intensity of the simulated wind field, generate more effective simulation points, and significantly improve the simulation efficiency. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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20 pages, 8028 KiB  
Article
Time-Averaged Wind Turbine Wake Flow Field Prediction Using Autoencoder Convolutional Neural Networks
by Zexia Zhang, Christian Santoni, Thomas Herges, Fotis Sotiropoulos and Ali Khosronejad
Energies 2022, 15(1), 41; https://doi.org/10.3390/en15010041 - 22 Dec 2021
Cited by 19 | Viewed by 2948
Abstract
A convolutional neural network (CNN) autoencoder model has been developed to generate 3D realizations of time-averaged velocity in the wake of the wind turbines at the Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility. Large-eddy simulations (LES) of the SWiFT site are [...] Read more.
A convolutional neural network (CNN) autoencoder model has been developed to generate 3D realizations of time-averaged velocity in the wake of the wind turbines at the Sandia National Laboratories Scaled Wind Farm Technology (SWiFT) facility. Large-eddy simulations (LES) of the SWiFT site are conducted using an actuator surface model to simulate the turbine structures to produce training and validation datasets of the CNN. The simulations are validated using the SpinnerLidar measurements of turbine wakes at the SWiFT site and the instantaneous and time-averaged velocity fields from the training LES are used to train the CNN. The trained CNN is then applied to predict 3D realizations of time-averaged velocity in the wake of the SWiFT turbines under flow conditions different than those for which the CNN was trained. LES results for the validation cases are used to evaluate the performance of the CNN predictions. Comparing the validation LES results and CNN predictions, we show that the developed CNN autoencoder model holds great potential for predicting time-averaged flow fields and the power production of wind turbines while being several orders of magnitude computationally more efficient than LES. Full article
(This article belongs to the Topic Artificial Intelligence and Sustainable Energy Systems)
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21 pages, 3632 KiB  
Article
A Frequency and Voltage Coordinated Control Strategy of Island Microgrid including Electric Vehicles
by Peixiao Fan, Song Ke, Salah Kamel, Jun Yang, Yonghui Li, Jinxing Xiao, Bingyan Xu and Ghamgeen Izat Rashed
Electronics 2022, 11(1), 17; https://doi.org/10.3390/electronics11010017 - 22 Dec 2021
Cited by 15 |&nbs