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Energies
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Artificial Intelligence and Machine Learning Applied to Energy Systems
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Artificial Intelligence and Machine Learning Applied to Energy Systems

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "F5: Artificial Intelligence and Smart Energy".

Deadline for manuscript submissions: closed (23 August 2023) | Viewed by 6508

Special Issue Editors

Dr. Faete Filho

E-Mail Website
Guest Editor
Department of Engineering, College of Engineering and Technology, East Carolina University, Greenville, CA 27858, USA
Interests: machine learning engineering; deep learning; predictive modeling; classification (binary/multiclass; support vector machines; decision trees); regression (linear/polynomial/logistic & SVM; random forests; ensemble learning); unsupervised learning (clustering; gaussian); dimensionality reduction; autoencoders; generative adversarial networks (GANs); deep computer vision; natural language processing (recurrent; convolutional neural networks & transformers); statistical & data science programming and data mining; power electronics; power systems; renewables (solar; wind; wave; etc); motor drives; solid state transformers; wide-bandgap devices; converter design & analysis; inverters drives & controls; and smart grid applications
Dr. Nic Herndon

E-Mail Website
Guest Editor
Department of Computer Science, College of Engineering and Technology, East Carolina University, Greenville, CA 27858, USA
Interests: artificial intelligence; machine learning; data science; big data analytics; computational biology
Dr. Rui Wu

E-Mail Website
Guest Editor
Department of Computer Science, College of Engineering and Technology, East Carolina University, Greenville, CA 27858, USA
Interests: data mining; data preprocessing; data postprocessing; virtual reality and augmented reality; big data visualization and interaction; imodel accuracy improvement; model as service; model implementation
Dr. Kamran Sartipi

E-Mail Website
Guest Editor
Department of Computer Science, College of Engineering and Technology, East Carolina University, Greenville, CA 27858, USA
Interests: knowledge engineering and data mining; cyber security and data privacy; user-behavior based access control; medical & health systems integration and informatics; service intelligence for cloud; decision support systems
Dr. Jason Yao

E-Mail Website
Guest Editor
Department of Engineering, College of Engineering and Technology, East Carolina University, Greenville, CA 27858, USA
Interests: wireless/wearable medical sensors; sensor networks for home environments; telemedicine; industrial process monitoring and control
Dr. Praveen Malali

E-Mail Website
Guest Editor
College of Engineering & Technology, Lafayette University, 730 High St, Easton, PA 18042, USA
Interests: renewable energy; energy conversion; energy efficiency; engineering education
Prof. Dr. Mehran Elahi

E-Mail Website
Guest Editor
Department of Technology, Elizabeth City State University, 1704 Weeksville Rd, Elizabeth City, NC 27909, USA
Interests: solid mechanics; experimental characterization; theoretical modeling and failure analysis of engineering materials; polymeric and ceramic matrix composite materials with aerospace, offshore and high temperature applications; renewable energy technologies
Dr. Thanh Toan Tran

E-Mail Website
Guest Editor
National Renewable Energy Laboratory (NREL), 15013 Denver West Parkway, Golden, CO 80401, USA
Interests: CFD; offshore wind turbines; semisubmersibles; tension leg platform

Special Issue Information

Dear Colleagues,

Artificial intelligence and machine learning have the potential to revolutionize the way we produce, distribute, and consume energy. They can help us optimize energy systems to be more efficient, reliable, and sustainable. They can be used to improve the forecasting of energy demand, allowing utilities to better match supply and demand and reduce waste. Many applications, already in place, help to optimize the operation of renewable energy systems, such as wind farms and solar panels, to increase their output and reduce their costs. In energy distribution and transmission networks, there are already several applications that enable real-time adjustments to meet changing demand and minimize losses. The use of artificial intelligence and machine learning in energy systems is an exciting and rapidly evolving field, and this Special Issue aims to present the most recent advances and research in this area.

This Special Issue aims to present and disseminate the most recent advances related to Artificial Intelligence and Machine Learning Applied to Energy Systems. Topics of interest for publication include, but are not limited to:

  • Energy demand forecasting and management
  • Optimization of renewable energy systems
  • Efficient and resilient operation of distribution and transmission networks
  • Predictive maintenance of energy equipment & systems
  • Integration of electric vehicles into the grid
  • Development of intelligent controls for energy systems
  • Prediction and prevention applied to energy system
  • Fault tolerance & resiliency
  • Energy storage systems
  • Analysis and interpretation of energy data for decision making purposes
  • Intelligent energy management systems
  • Deep Learning for Predictive Maintenance in Renewable Energy Systems
  • AI-Powered Energy Management in Smart Buildings
  • Machine Learning-Based Demand Forecasting for Electric Vehicle Charging Stations
  • Artificial Neural Networks for Optimal Operation of Microgrid Systems
  • Reinforcement Learning for Energy Trading in Smart Grids
  • Natural Language Processing for Automated Generation of Energy Reports
  • Transfer Learning for Solar Irradiance Prediction
  • Evaluating the Use of Deep Learning for Enhancing the Accuracy of Wind Power Forecasts
  • Explainable AI for Energy System Optimization
  • Application of Deep Learning for Improving the Efficiency of Distributed Energy Resources

Dr. Faete Filho
Dr. Nic Herndon
Dr. Rui Wu
Dr. Kamran Sartipi
Dr. Jason Yao
Dr. Praveen Malali
Prof. Dr. Mehran Elahi
Dr. Thanh Toan Tran
Guest Editors

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All submissions that pass pre-check are peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Energies is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 2600 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • artificial intelligence
  • machine learning
  • energy systems
  • renewable energy
  • demand & forecasting
  • energy generation, transmission, and distribution
  • equipment maintenance
  • electric vehicles
  • energy storage
  • energy data analysis
  • energy management
  • intelligent control systems

Published Papers (5 papers)

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Research

20 pages, 4924 KiB  
Article
Explainable Approaches for Forecasting Building Electricity Consumption
by Nikos Sakkas, Sofia Yfanti, Pooja Shah, Nikitas Sakkas, Christina Chaniotakis, Costas Daskalakis, Eduard Barbu and Marharyta Domnich
Energies 2023, 16(20), 7210; https://doi.org/10.3390/en16207210 - 23 Oct 2023
Cited by 1 | Viewed by 1005
Abstract
Building electric energy is characterized by a significant increase in its uses (e.g., vehicle charging), a rapidly declining cost of all related data collection, and a proliferation of smart grid concepts, including diverse and flexible electricity pricing schemes. Not surprisingly, an increased number [...] Read more.
Building electric energy is characterized by a significant increase in its uses (e.g., vehicle charging), a rapidly declining cost of all related data collection, and a proliferation of smart grid concepts, including diverse and flexible electricity pricing schemes. Not surprisingly, an increased number of approaches have been proposed for its modeling and forecasting. In this work, we place our emphasis on three forecasting-related issues. First, we look at the forecasting explainability, that is, the ability to understand and explain to the user what shapes the forecast. To this extent, we rely on concepts and approaches that are inherently explainable, such as the evolutionary approach of genetic programming (GP) and its associated symbolic expressions, as well as the so-called SHAP (SHapley Additive eXplanations) values, which is a well-established model agnostic approach for explainability, especially in terms of feature importance. Second, we investigate the impact of the training timeframe on the forecasting accuracy; this is driven by the realization that fast training would allow for faster deployment of forecasting in real-life solutions. And third, we explore the concept of counterfactual analysis on actionable features, that is, features that the user can really act upon and which therefore present an inherent advantage when it comes to decision support. We have found that SHAP values can provide important insights into the model explainability. In our analysis, GP models demonstrated superior performance compared to neural network-based models (with a 20–30% reduction in Root Mean Square Error (RMSE)) and time series models (with a 20–40% lower RMSE), but a rather questionable potential to produce crisp and insightful symbolic expressions, allowing a better insight into the model performance. We have also found and reported here on an important potential, especially for practical, decision support, of counterfactuals built on actionable features, and short training timeframes. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applied to Energy Systems)
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16 pages, 1251 KiB  
Article
Transfer Learning Prediction Performance of Chillers for Neural Network Models
by Hongwen Dou and Radu Zmeureanu
Energies 2023, 16(20), 7149; https://doi.org/10.3390/en16207149 - 19 Oct 2023
Viewed by 737
Abstract
Building automation systems installed in large commercial buildings record sub-hourly measurements from hundreds of sensors. The use of such large datasets are challenging because of missing and erroneous data, which can prevent the development of accurate prediction models of the performance of heating, [...] Read more.
Building automation systems installed in large commercial buildings record sub-hourly measurements from hundreds of sensors. The use of such large datasets are challenging because of missing and erroneous data, which can prevent the development of accurate prediction models of the performance of heating, ventilation, and air-conditioning equipment. The use of the transfer learning (TL) method for building applications attracted researchers to solve the problems created by small and incomplete datasets. This paper verifies the hypothesis that the deep neural network models that are pre-trained for one chiller (called the source chiller) with a small dataset of measurements from July 2013 could be applied successfully, by using TL strategies, for the prediction of the operation performance of another chiller (called the target chiller) with different datasets that were recorded during the cooling season of 2016. Measurements from a university campus are used as a case study. The results show that the initial hypothesis of this paper is confirmed. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applied to Energy Systems)
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18 pages, 1412 KiB  
Article
Meta In-Context Learning: Harnessing Large Language Models for Electrical Data Classification
by Mi Zhou, Fusheng Li, Fan Zhang, Junhao Zheng and Qianli Ma
Energies 2023, 16(18), 6679; https://doi.org/10.3390/en16186679 - 18 Sep 2023
Viewed by 1528
Abstract
The evolution of communication technology has driven the demand for intelligent power grids and data analysis in power systems. However, obtaining and annotating electrical data from intelligent terminals is time-consuming and challenging. We propose Meta In-Context Learning (M-ICL), a new approach that harnesses [...] Read more.
The evolution of communication technology has driven the demand for intelligent power grids and data analysis in power systems. However, obtaining and annotating electrical data from intelligent terminals is time-consuming and challenging. We propose Meta In-Context Learning (M-ICL), a new approach that harnesses large language models to classify time series electrical data, which largely alleviates the need for annotated data when adapting to new tasks. The proposed M-ICL consists of two stages: meta-training and meta-testing. In meta-training, the model is trained on various tasks that have an adequate amount of training data. The meta-training stage aims to learn the mapping between electrical data and the embedding space of large language models. In the meta-testing stage, the trained model makes predictions on new tasks. By utilizing the in-context learning ability of large language models, M-ICL adapts models to new tasks effectively with only a few annotated instances (e.g., 1–5 training instances per class). Our contributions lie in the new application of large language models to electrical data classification and the introduction of M-ICL to improve the classification performance with the strong in-context learning ability of large language models. Furthermore, we conduct extensive experiments on 13 real-world datasets, and the experimental results show that the proposed M-ICL improves the average accuracy over all datasets by 19.06%, 12.06%, and 6.63% when only one, two, and five training instances for each class are available, respectively. In summary, M-ICL offers a promising solution to the challenges of electrical data classification. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applied to Energy Systems)
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15 pages, 2121 KiB  
Article
Using Deep Neural Network Methods for Forecasting Energy Productivity Based on Comparison of Simulation and DNN Results for Central Poland—Swietokrzyskie Voivodeship
by Michal Pikus and Jarosław Wąs
Energies 2023, 16(18), 6632; https://doi.org/10.3390/en16186632 - 15 Sep 2023
Cited by 2 | Viewed by 1187
Abstract
Forecasting electricity demand is of utmost importance for ensuring the stability of the entire energy sector. However, predicting the future electricity demand and its value poses a formidable challenge due to the intricate nature of the processes influenced by renewable energy sources. Within [...] Read more.
Forecasting electricity demand is of utmost importance for ensuring the stability of the entire energy sector. However, predicting the future electricity demand and its value poses a formidable challenge due to the intricate nature of the processes influenced by renewable energy sources. Within this piece, we have meticulously explored the efficacy of fundamental deep learning models designed for electricity forecasting. Among the deep learning models, we have innovatively crafted recursive neural networks (RNNs) predominantly based on LSTM and combined architectures. The dataset employed was procured from a SolarEdge designer. The dataset encompasses daily records spanning the past year, encompassing an exhaustive collection of parameters extracted from solar farm (based on location in Central Europe (Poland Swietokrzyskie Voivodeship)). The experimental findings unequivocally demonstrated the exceptional superiority of the LSTM models over other counterparts concerning forecasting accuracy. Consequently, we compared multilayer DNN architectures with results provided by the simulator. The measurable results of both DNN models are multi-layer LSTM-only accuracy based on R2—0.885 and EncoderDecoderLSTM R2—0.812. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applied to Energy Systems)
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22 pages, 630 KiB  
Article
HyMOTree: Automatic Hyperparameters Tuning for Non-Technical Loss Detection Based on Multi-Objective and Tree-Based Algorithms
by Francisco Jonatas Siqueira Coelho, Allan Rivalles Souza Feitosa, André Luís Michels Alcântara, Kaifeng Li, Ronaldo Ferreira Lima, Victor Rios Silva and Abel Guilhermino da Silva-Filho
Energies 2023, 16(13), 4971; https://doi.org/10.3390/en16134971 - 27 Jun 2023
Viewed by 1317
Abstract
The most common methods to detect non-technical losses involve Deep Learning-based classifiers and samples of consumption remotely collected several times a day through Smart Meters (SMs) and Advanced Metering Infrastructure (AMI). This approach requires a huge amount of data, and training is computationally [...] Read more.
The most common methods to detect non-technical losses involve Deep Learning-based classifiers and samples of consumption remotely collected several times a day through Smart Meters (SMs) and Advanced Metering Infrastructure (AMI). This approach requires a huge amount of data, and training is computationally expensive. However, most energy meters in emerging countries such as Brazil are technologically limited. These devices can measure only the accumulated energy consumption monthly. This work focuses on detecting energy theft in scenarios without AMI and SM. We propose a strategy called HyMOTree intended for the hyperparameter tuning of tree-based algorithms using different multiobjective optimization strategies. Our main contributions are associating different multiobjective optimization strategies to improve the classifier performance and analyzing the model’s performance given different probability cutoff operations. HyMOTree combines NSGA-II and GDE-3 with Decision Tree, Random Forest, and XGboost. A dataset provided by a Brazilian power distribution company CPFL ENERGIA™ was used, and the SMOTE technique was applied to balance the data. The results show that HyMOTree performed better than the random search method, and then, the combination between Random Forest and NSGA-II achieved 0.95 and 0.93 for Precision and F1-Score, respectively. Field studies showed that inspections guided by HyMOTree achieved an accuracy of 76%. Full article
(This article belongs to the Special Issue Artificial Intelligence and Machine Learning Applied to Energy Systems)
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