Search for Articles:
Title / Keyword
Author / Affiliation / Email
Journal
Article Type
 
 
Advanced Search
 
Section
Special Issue
Volume
Issue
Number
Page
 
5.5
3.2
Journals
Energies
Special Issues
Big Data and Advanced Analytics in Energy Systems and Applications
energies-logo
Submit to Energies Review for Energies Propose a Special Issue

Journal Menu

► Journal Menu

Journal Browser

► Journal Browser
share announcement

Big Data and Advanced Analytics in Energy Systems and Applications

A special issue of Energies (ISSN 1996-1073). This special issue belongs to the section "A: Sustainable Energy".

Deadline for manuscript submissions: closed (31 October 2022) | Viewed by 34458

Special Issue Editors

Prof. Dr. Constantinos S. Psomopoulos

E-Mail Website
Guest Editor
High Voltage and Energy Systems Research Lab, Department of Electrical and Electronics Engineering, University of West Attica, 12244 Egaleo, Greece
Interests: eco design and energy efficiency; materials and energy recovery from wastes; high-voltage engineering; electrical measurements and high field effects; electromechanical installations and apparatus
Special Issues, Collections and Topics in MDPI journals
Dr. Helen C. Leligou

E-Mail Website
Guest Editor
Department of Industrial Design and Production Engineering, University of West Attica, GR-122 44 Egaleo, Greece
Interests: information and communication technologies including: routing protocols and trust management in wireless sensor networks; control plane technologies in broadband networks including HFC, PON, WDM metro and core networks; industrial, embedded, and network system design and development; blockchain technologies
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Ferdinanda Ponci

E-Mail Website
Guest Editor
Institute for Automation of Complex Power Systems, RWTH Aachen University, 52064 Aachen, Germany
Interests: electrical power engineering; distributed generation; measurement, monitoring, and automation of electrical distribution systems; distributed control for power systems, monitoring, and control of active electrical distribution grids and urban energy grids; power hardware-in-the-loop platform for the testing of monitoring systems; multiagent control system
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Josep M. Guerrero

grade E-Mail Website
Guest Editor
Department of Electronic Engineering, Technical University of Catalonia, Barcelona, Spain
Interests: microgrids; renewable energy systems; neuroscience-based artificial intelligence; digital twins; cybersecurity
Special Issues, Collections and Topics in MDPI journals
Prof. Dr. Elisa Peñalvo-López

E-Mail Website
Guest Editor
Electronic Engineering Department, Universitat Politècnica de València (IIE-UPV), 46022 Valencia, Spain
Interests: distributed energy resources and hybrid renewable systems; energy efficiency; energy sustainability and energy markets
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

Energy systems are becoming more and more complex and advanced as new concepts of energy production and utilization arise from technological developments. Sensors are collecting huge amounts of data during generation, transmission, distribution, and consumption of energy. The increasing complexity of energy systems requires finding new ways to utilize engineering experience and data collection to improve decision making. The energy markets are becoming broader as energy networks are expanded and interconnected. We are now facing the era of Internet of Thing and Internet of Energy. In this context, a new area of paramount importance is arising in the energy industry, big data in energy systems and applications. Operation data on supervisory control and data acquisition, energy management, distribution management, distributed energy resource management, and many more applications are now far too complex to be treated with legacy approaches and methods. The research work and many applications have proven the paramount importance of advancing bid data analytics to improve the design, operation, and maintenance of energy systems and have led to new advanced energy application.

This Special Issue intends to collect these different applications of big data in energy systems and present the diversity of possibilities of new methods, ideas, and solutions for energy applications. Original research papers, reviews, successful case studies and applications as well as opinion papers with high quality and novelty on “Big Data and Advanced Analytics in Energy Systems and Applications” are more than welcome.

Prof. Dr. Constantinos S. Psomopoulos
Assoc. Prof. Helen C. Leligou
Prof. Dr. Ferdinanda Ponci
Prof. Dr. Josep M. Guerrero
Prof. Dr. Elisa Peñalvo-López
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

  • Big data applications in energy systems
  • Distributed sensors systems for data collection in energy systems
  • Utility analytics
  • Machine learning and algorithms in energy
  • Big data for energy transition
  • Big data in critical energy infrastructure
  • Big data analytics in microgrids
  • Preventive and pervasive analytics in energy
  • Advance energy applications of big data
  • IoT and cloud in energy systems
  • Data mining in energy systems
  • Prediction analytics in energy systems
  • Advanced energy systems modeling using data analytics
  • Data in control applications for energy systems
  • Digital twins and big data in energy
  • PMUs and big data
  • Big data in energy markets
  • Big data in renewables
  • Smart grids and big data
  • Big data in power system operation and control
  • Data classification and clustering in energy systems
  • Big data analytics in clean energy application
  • Big Data and the UN Sustainability Goals in energy
  • Data analytics in electric traction systems
  • Data analytics in load forecasting
  • Data analytics and electric vehicles
  • Artificial intelligence in energy systems
  • Advance statistics for energy
  • Internet of Energy
  • Collection and visualization of data
  • Data infrastructure for utilities
  • Data and system awareness
  • Digital transformation of energy systems
  • Real-time data management
  • Data based energy system optimization
  • Big data and energy system reliability
  • Operations data on supervisory control and data acquisition
  • Data in energy management
  • Distributed energy resource management
  • Energy data in sustainable energy scenarios

Published Papers (15 papers)

Download All Papers
Order results
Result details
Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:

Research

17 pages, 5472 KiB  
Article
Condition Assessment of Power Transformers through DGA Measurements Evaluation Using Adaptive Algorithms and Deep Learning
by Dimitris A. Barkas, Stavros D. Kaminaris, Konstantinos K. Kalkanis, George Ch. Ioannidis and Constantinos S. Psomopoulos
Energies 2023, 16(1), 54; https://doi.org/10.3390/en16010054 - 21 Dec 2022
Cited by 7 | Viewed by 1604
Abstract
Condition assessment for critical infrastructure is a key factor for the wellbeing of the modern human. Especially for the electricity network, specific components such as oil-immersed power transformers need to be monitored for their operating condition. Classic approaches for the condition assessment of [...] Read more.
Condition assessment for critical infrastructure is a key factor for the wellbeing of the modern human. Especially for the electricity network, specific components such as oil-immersed power transformers need to be monitored for their operating condition. Classic approaches for the condition assessment of oil-immersed power transformers have been proposed in the past, such as the dissolved gases analysis and their respective concentration measurements for insulating oils. However, these approaches cannot always correctly (and in many cases not at all) classify the problems in power transformers. In the last two decades, novel approaches are implemented so as to address this problem, including artificial intelligence with neural networks being one form of algorithm. This paper focuses on the implementation of an adaptive number of layers and neural networks, aiming to increase the accuracy of the operating condition of oil-immersed power transformers. This paper also compares the use of various activation functions and different transfer functions other than the neural network implemented. The comparison incorporates the accuracy and total structure size of the neural network. Full article
(This article belongs to the Special Issue Big Data and Advanced Analytics in Energy Systems and Applications)
►▼ Show Figures

Figure 1

23 pages, 859 KiB  
Article
Semantic-Similarity-Based Schema Matching for Management of Building Energy Data
by Zhiyu Pan, Guanchen Pan and Antonello Monti
Energies 2022, 15(23), 8894; https://doi.org/10.3390/en15238894 - 24 Nov 2022
Cited by 3 | Viewed by 2016
Abstract
The increase in heterogeneous data in the building energy domain creates a difficult challenge for data integration. Schema matching, which maps the raw data from the building energy domain to a generic data model, is the necessary step in data integration and provides [...] Read more.
The increase in heterogeneous data in the building energy domain creates a difficult challenge for data integration. Schema matching, which maps the raw data from the building energy domain to a generic data model, is the necessary step in data integration and provides a unique representation. Only a small amount of labeled data for schema matching exists and it is time-consuming and labor-intensive to manually label data. This paper applies semantic-similarity methods to the automatic schema-mapping process by combining knowledge from natural language processing, which reduces the manual effort in heterogeneous data integration. The active-learning method is applied to solve the lack-of-labeled-data problem in schema matching. The results of the schema matching with building-energy-domain data show the pre-trained language model provides a massive improvement in the accuracy of schema matching and the active-learning method greatly reduces the amount of labeled data required. Full article
(This article belongs to the Special Issue Big Data and Advanced Analytics in Energy Systems and Applications)
►▼ Show Figures

Figure 1

21 pages, 6668 KiB  
Article
Performance Analysis of Thermal Image Processing-Based Photovoltaic Fault Detection and PV Array Reconfiguration—A Detailed Experimentation
by Srinivasan Alwar, Devakirubakaran Samithas, Meenakshi Sundaram Boominathan, Praveen Kumar Balachandran and Lucian Mihet-Popa
Energies 2022, 15(22), 8450; https://doi.org/10.3390/en15228450 - 11 Nov 2022
Cited by 17 | Viewed by 1872
Abstract
Due to the flexibility, sustainability, affordability, and ease of installation of solar photovoltaic systems, their use has significantly increased over the past two decades. The performance of a solar PV system can be constrained by a variety of external conditions, including hotspots, partial [...] Read more.
Due to the flexibility, sustainability, affordability, and ease of installation of solar photovoltaic systems, their use has significantly increased over the past two decades. The performance of a solar PV system can be constrained by a variety of external conditions, including hotspots, partial shade, and other minor faults. This causes the PV system to permanently fail and power losses. The power output in a partially shaded solar system is improved in this work by the introduction of a fault classifier based on thermal image analysis with a reconfiguration algorithm. For that purpose, the entire PV array is divided into two parts, with one of these being the male part and the other being the female part. MOSFET switches are used to build the switching matrix circuit that connects these parts. The Flir T420bx thermal camera captures thermal pictures, and MATLAB/Simulink® is used to extract the image properties. The pairing reconfiguration pattern is found using an algorithm based on image processing and the image attributes. The switching signals to the switching circuit are triggered by an Arduino controller. The image attributes of the thermal images may also be used to categorize PV system defects. This reconfiguration technique is easy, simple to use, and it can also be used to check the health of each PV module. The performance of the proposed work was validated using a 5 kW PV system with a 4 × 5 TCT array configuration at Sethu Institute of Technology’s renewable energy lab in India. The proposed method was simulated using the MATLAB-Simulink software program, and the outcomes were verified on different hardware setups. Full article
(This article belongs to the Special Issue Big Data and Advanced Analytics in Energy Systems and Applications)
►▼ Show Figures

Figure 1

20 pages, 2317 KiB  
Article
Re-Engineering of Marketing for SMEs in Energy Market through Modeling Customers’ Strategic Behavior
by Despina S. Giakomidou, Athanasios Kriemadis, Dimitrios K. Nasiopoulos and Dimitrios Mastrakoulis
Energies 2022, 15(21), 8179; https://doi.org/10.3390/en15218179 - 02 Nov 2022
Cited by 3 | Viewed by 1762
Abstract
In recent years, the energy market has seen an increase in small and medium enterprises (SMEs) participating in the sector and providing relevant services to customers. The energy sector SMEs need to acknowledge whether reengineering their marketing strategy by modeling customers’ website behavior [...] Read more.
In recent years, the energy market has seen an increase in small and medium enterprises (SMEs) participating in the sector and providing relevant services to customers. The energy sector SMEs need to acknowledge whether reengineering their marketing strategy by modeling customers’ website behavior could enhance their digital marketing efficiency. Web Analytics refers to the extracted data of customers’ behavior from firms’ websites, a subclass of big data (big masses of uncategorized data information). This study aims to provide insights regarding the impact that energy SMEs’ web analytics has on their digital marketing efficiency as a marketing reengineering process. The paper’s methodology begins with the retrieval of behavioral website data from SMEs in the energy sector, followed by regression and correlation analyses and the development of simulation models with Fuzzy Cognitive Mapping (FCM). Research results showed that customer behavioral data originating from SMEs’ websites can effectively impact key digital marketing performance indicators, such as increasing new visits and reducing organic costs and bounce rate (digital marketing analytics). SMEs in the energy sector can potentially increase their website visibility and customer base by re-engineering their marketing strategy and utilizing customers’ behavioral analytic data. Full article
(This article belongs to the Special Issue Big Data and Advanced Analytics in Energy Systems and Applications)
►▼ Show Figures

Figure 1

55 pages, 19590 KiB  
Article
SENERGY: A Novel Deep Learning-Based Auto-Selective Approach and Tool for Solar Energy Forecasting
by Ghadah Alkhayat, Syed Hamid Hasan and Rashid Mehmood
Energies 2022, 15(18), 6659; https://doi.org/10.3390/en15186659 - 12 Sep 2022
Cited by 10 | Viewed by 2065
Abstract
Researchers have made great progress in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracy, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weather due to the [...] Read more.
Researchers have made great progress in developing cutting-edge solar energy forecasting methods. However, these methods are far from optimal in terms of their accuracy, generalizability, benchmarking, and other requirements. Particularly, no single method performs well across all climates and weather due to the large variations in meteorological data. This paper proposes SENERGY (an acronym for sustainable energy), a novel deep learning-based auto-selective approach and tool that, instead of generalizing a specific model for all climates, predicts the best performing deep learning model for global horizontal irradiance (GHI) forecasting in terms of forecasting error. The approach is based on carefully devised deep learning methods and feature sets created through an extensive analysis of deep learning forecasting and classification methods using ten meteorological datasets from three continents. We analyze the tool in great detail through a variety of metrics and means for performance analysis, visualization, and comparison of solar forecasting methods. SENERGY outperforms existing methods in all performance metrics including mean absolute error (MAE), root mean square error (RMSE), mean absolute percentage error (MAPE), the normalized versions of these three metrics (nMAE, nRMSE, nMAPE), forecast skill (FS), and relative forecasting error. The long short-term memory-autoencoder model (LSTM-AE) outperformed the other four forecasting models and achieved the best results (nMAE = nRMSE = nMAPE = 0.02). The LSTM-AE model is the most accurate in all weather conditions. Predictions for sunny days are more accurate than for cloudy days as well as for summer compared to winter. SENERGY can predict the best forecasting model with 81% accuracy. The proposed auto-selective approach can be extended to other research problems, such as wind energy forecasting, and to predict forecasting models based on different criteria such as the energy required or speed of model execution, different input features, different optimizations of the same models, or other user preferences. Full article
(This article belongs to the Special Issue Big Data and Advanced Analytics in Energy Systems and Applications)
►▼ Show Figures

Graphical abstract

20 pages, 6313 KiB  
Article
A Comprehensive Assessment of Products Management and Energy Recovery from Waste Products in the United States
by A. C. (Thanos) Bourtsalas, Tianxiao Shen and Yixi Tian
Energies 2022, 15(18), 6581; https://doi.org/10.3390/en15186581 - 08 Sep 2022
Cited by 1 | Viewed by 1685
Abstract
This study uses the U.S. EPA data and classification of products, which includes three main categories: durables with a lifetime over 3 years, non-durables with a lifetime below 3 years, and containers and packages, which are consumed within one year. It builds connections [...] Read more.
This study uses the U.S. EPA data and classification of products, which includes three main categories: durables with a lifetime over 3 years, non-durables with a lifetime below 3 years, and containers and packages, which are consumed within one year. It builds connections between the management of waste products and the energy sector, by evaluating the potential contribution of such products to the U.S. energy grid, and assessing the opportunity to substitute fossil fuels, both for electricity and residential heat production. Finally, this study conducts a vis-à-vis comparison between the U.S. and the EU progress on waste management, and the associated GHG emissions. Sankey diagrams were produced to represent the flows of products management from 1990 to 2018, and the results were assessed by considering the amounts produced, the composition, and the disposition methods used, the energy potential of waste products landfilled, and the associated greenhouse gases (GHG) emissions. The results indicate that the recycling of containers and packages have increased significantly during the 28-year period and became the dominant method of managing such products in the U.S. in 2015. Durable and non-durable products are mainly landfilled, and the situation has remained unchanged in the 2010s. Assuming that 30% of waste products landfilled in the U.S. were combusted for energy instead, it would have resulted in the substitution of <5% of fossil fuels used for electricity, but up to a 68% substitution of fossil fuels, such as propane, used for residential space and water heating. In the U.S., over 85% of GHG emissions are associated with the landfilling of waste materials, and although improvements in capturing and beneficially utilizing methane are implemented, the total GHG emissions have remained almost the same since 2015, with a tendency to increase. The European experience has shown that recycling and waste-to-energy are complementary in diverting materials from landfills, in enhancing energy security, and in significantly reducing GHG emissions from waste management. Future directions are discussed. Full article
(This article belongs to the Special Issue Big Data and Advanced Analytics in Energy Systems and Applications)
►▼ Show Figures

Figure 1

39 pages, 2182 KiB  
Article
Systematic Review on Impact of Different Irradiance Forecasting Techniques for Solar Energy Prediction
by Konduru Sudharshan, C. Naveen, Pradeep Vishnuram, Damodhara Venkata Siva Krishna Rao Kasagani and Benedetto Nastasi
Energies 2022, 15(17), 6267; https://doi.org/10.3390/en15176267 - 28 Aug 2022
Cited by 20 | Viewed by 3259
Abstract
As non-renewable energy sources are in the verge of exhaustion, the entire world turns towards renewable sources to fill its energy demand. In the near future, solar energy will be a major contributor of renewable energy, but the integration of unreliable solar energy [...] Read more.
As non-renewable energy sources are in the verge of exhaustion, the entire world turns towards renewable sources to fill its energy demand. In the near future, solar energy will be a major contributor of renewable energy, but the integration of unreliable solar energy sources directly into the grid makes the existing system complex. To reduce the complexity, a microgrid system is a better solution. Solar energy forecasting models improve the reliability of the solar plant in microgrid operations. Uncertainty in solar energy prediction is the challenge in generating reliable energy. Employing, understanding, training, and evaluating several forecasting models with available meteorological data will ensure the selection of an appropriate forecast model for any particular location. New strategies and approaches emerge day by day to increase the model accuracy, with an ultimate objective of minimizing uncertainty in forecasting. Conventional methods include a lot of differential mathematical calculations. Large data availability at solar stations make use of various Artificial Intelligence (AI) techniques for computing, forecasting, and predicting solar radiation energy. The recent evolution of ensemble and hybrid models predicts solar radiation accurately compared to all the models. This paper reviews various models in solar irradiance and power estimation which are tabulated by classification types mentioned. Full article
(This article belongs to the Special Issue Big Data and Advanced Analytics in Energy Systems and Applications)
►▼ Show Figures

Figure 1

19 pages, 1732 KiB  
Article
On Deploying the Internet of Energy with 5G Open RAN Technology including Beamforming Mechanism
by Jordi Mongay Batalla, Mustafa Moshin, Constandinos X. Mavromoustakis, Krzysztof Wesołowski, George Mastorakis and Karolina Krzykowska-Piotrowska
Energies 2022, 15(7), 2429; https://doi.org/10.3390/en15072429 - 25 Mar 2022
Cited by 9 | Viewed by 1972
Abstract
The Internet of Energy is the deployment of IoT technology within energy systems (including distributed power monitoring and measuring points, energy plant sensors, points of distribution) to increase the efficiency of the whole infrastructure while decreasing energy waste. Due to criticality and the [...] Read more.
The Internet of Energy is the deployment of IoT technology within energy systems (including distributed power monitoring and measuring points, energy plant sensors, points of distribution) to increase the efficiency of the whole infrastructure while decreasing energy waste. Due to criticality and the extension of the Internet of Energy, it needs an underlying network with vast coverage and high-efficiency parameters. In this paper, we argue that the 5G network is suitable for the Internet of Energy and present a concrete 5G implementation based on Open RAN that may gain in flexibility while reducing costs. In our simulations, we model and validate beamforming mechanism in Open RAN 5G and show that beamforming may achieve high-efficiency parameters that the Internet of Energy requires. Full article
(This article belongs to the Special Issue Big Data and Advanced Analytics in Energy Systems and Applications)
►▼ Show Figures

Figure 1

16 pages, 27261 KiB  
Article
Big Data Analytics for Spatio-Temporal Service Orders Demand Forecasting in Electric Distribution Utilities
by Vitor Hugo Ferreira, Rubens Lucian da Silva Correa, Angelo Cesar Colombini, Márcio Zamboti Fortes, Flávio Luis de Mello, Fernando Carvalho Cid de Araujo and Natanael Rodrigues Pereira
Energies 2021, 14(23), 7991; https://doi.org/10.3390/en14237991 - 30 Nov 2021
Cited by 3 | Viewed by 2119
Abstract
This paper presents a big data analytics-based model developed for electric distribution utilities aiming to forecast the demand of service orders (SOs) on a spatio-temporal basis. Being fed by robust history and location data from a database provided by an energy utility that [...] Read more.
This paper presents a big data analytics-based model developed for electric distribution utilities aiming to forecast the demand of service orders (SOs) on a spatio-temporal basis. Being fed by robust history and location data from a database provided by an energy utility that is using this innovative system, the algorithm automatically forecasts the number of SOs that will need to be executed in each location in several time steps (hourly, monthly and yearly basis). The forecasted emergency SOs demand, which is related to energy outages, are stochastically distributed, projecting the impacted consumers and its individual interruption indexes. This spatio-temporal forecasting is the main input for a web-based platform for optimal bases allocation, field team sizing and scheduling implemented in the eleven distribution utilities of Energisa group in Brazil. Full article
(This article belongs to the Special Issue Big Data and Advanced Analytics in Energy Systems and Applications)
►▼ Show Figures

Figure 1

22 pages, 19857 KiB  
Article
Decarbonization of Distribution Transformers Based on Current Reduction: Economic and Environmental Impacts
by Vicente León-Martínez, Clara Andrada-Monrós, Laura Molina-Cañamero, Jorge Cano-Martínez and Elisa Peñalvo-López
Energies 2021, 14(21), 7207; https://doi.org/10.3390/en14217207 - 02 Nov 2021
Cited by 3 | Viewed by 1641
Abstract
Well-known industrial practice efficiency improvement techniques, such as reactive compensation, load balancing, and harmonic filtering, are used in this paper to reduce energy losses in distribution transformers, and therefore, to decrease carbon dioxide emissions and economic costs in the operation of these transformers. [...] Read more.
Well-known industrial practice efficiency improvement techniques, such as reactive compensation, load balancing, and harmonic filtering, are used in this paper to reduce energy losses in distribution transformers, and therefore, to decrease carbon dioxide emissions and economic costs in the operation of these transformers. Load balancing is carried out by monitoring the values of the angles of the active and reactive components of the vector unbalanced power. Likewise, the application of Order 3/2020 of the Spanish National Markets and Competition Commission is described, in detail, for the calculation of the economic costs derived from the transformer energy losses caused by the load currents and the penalties due to transformer energy deliveries with capacitive power factors. Finally, all these improvement techniques are applied to determine savings in carbon dioxide emissions and costs on the electricity bill of an actual 1000 kVA distribution transformer that supplies a commercial and night-entertainment area. The results of this application case reveal that cost reductions due to energy loss savings are modest, but the reduction in carbon dioxide emissions and the savings in penalties for capacitive reactive supplies are significant. Full article
(This article belongs to the Special Issue Big Data and Advanced Analytics in Energy Systems and Applications)
►▼ Show Figures

Figure 1

16 pages, 410 KiB  
Article
Comparative Analysis of Load Forecasting Models for Varying Time Horizons and Load Aggregation Levels
by Leonard Burg, Gonca Gürses-Tran, Reinhard Madlener and Antonello Monti
Energies 2021, 14(21), 7128; https://doi.org/10.3390/en14217128 - 01 Nov 2021
Cited by 11 | Viewed by 2357
Abstract
Power system operators are confronted with a multitude of new forecasting tasks to ensure a constant supply security despite the decreasing number of fully controllable energy producers. With this paper, we aim to facilitate the selection of suitable forecasting approaches for the load [...] Read more.
Power system operators are confronted with a multitude of new forecasting tasks to ensure a constant supply security despite the decreasing number of fully controllable energy producers. With this paper, we aim to facilitate the selection of suitable forecasting approaches for the load forecasting problem. First, we provide a classification of load forecasting cases in two dimensions: temporal and hierarchical. Then, we identify typical features and models for forecasting and compare their applicability in a structured manner depending on six previously defined cases. These models are compared against real data in terms of their computational effort and accuracy during development and testing. From this comparative analysis, we derive a generic guide for the selection of the best prediction models and features per case. Full article
(This article belongs to the Special Issue Big Data and Advanced Analytics in Energy Systems and Applications)
►▼ Show Figures

Figure 1

23 pages, 7596 KiB  
Article
An Efficient Parameter Estimation Algorithm for Proton Exchange Membrane Fuel Cells
by Mohamed Abdel-Basset, Reda Mohamed and Victor Chang
Energies 2021, 14(21), 7115; https://doi.org/10.3390/en14217115 - 01 Nov 2021
Cited by 14 | Viewed by 1936
Abstract
The proton exchange membrane fuel cell (PEMFC) is a favorable renewable energy source to overcome environmental pollution and save electricity. However, the mathematical model of the PEMFC contains some unknown parameters which have to be accurately estimated to build an accurate PEMFC model; [...] Read more.
The proton exchange membrane fuel cell (PEMFC) is a favorable renewable energy source to overcome environmental pollution and save electricity. However, the mathematical model of the PEMFC contains some unknown parameters which have to be accurately estimated to build an accurate PEMFC model; this problem is known as the parameter estimation of PEMFC and belongs to the optimization problem. Although this problem belongs to the optimization problem, not all optimization algorithms are suitable to solve it because it is a nonlinear and complex problem. Therefore, in this paper, a new optimization algorithm known as the artificial gorilla troops optimizer (GTO), which simulates the collective intelligence of gorilla troops in nature, is adapted for estimating this problem. However, the GTO is suffering from local optima and low convergence speed problems, so a modification based on replacing its exploitation operator with a new one, relating the exploration and exploitation according to the population diversity in the current iteration, has been performed to improve the exploitation operator in addition to the exploration one. This modified variant, named the modified GTO (MGTO), has been applied for estimating the unknown parameters of three PEMFC stacks, 250 W stack, BCS-500W stack, and SR-12 stack, used widely in the literature, based on minimizing the error between the measured and estimated data points as the objective function. The outcomes obtained by applying the GTO and MGTO on those PEMFC stacks have been extensively compared with those of eight well-known optimization algorithms using various performance analyses, best, average, worst, standard deviation (SD), CPU time, mean absolute percentage error (MAPE), and mean absolute error (MAE), in addition to the Wilcoxon rank-sum test, to show which one is the best for solving this problem. The experimental findings show that MGTO is the best for all performance metrics, but CPU time is competitive among all algorithms. Full article
(This article belongs to the Special Issue Big Data and Advanced Analytics in Energy Systems and Applications)
►▼ Show Figures

Figure 1

18 pages, 917 KiB  
Article
A New Clustering Approach for Automatic Oscillographic Records Segmentation
by Vitor Hugo Ferreira, André da Costa Pinho, Dickson Silva de Souza and Bárbara Siqueira Rodrigues
Energies 2021, 14(20), 6778; https://doi.org/10.3390/en14206778 - 18 Oct 2021
Cited by 2 | Viewed by 1496
Abstract
The analysis of waveforms related to transient events is an important task in power system maintenance. Currently, electric power systems are monitored by several event recorders called phasor measurement units (PMUs) which generate a large amount of data. The number of records is [...] Read more.
The analysis of waveforms related to transient events is an important task in power system maintenance. Currently, electric power systems are monitored by several event recorders called phasor measurement units (PMUs) which generate a large amount of data. The number of records is so high that it makes human analysis infeasible. An alternative way of solving this problem is to group events in similar classes so that it is no longer necessary to analyze all the events, but only the most representative of each class. Several automatic clustering algorithms have been proposed in the literature. Most of these algorithms use validation indexes to rank the partitioning quality and, consequently, find the optimal number of clusters. However, this issue remains open, as each index has its own performance highly dependent on the data spatial distribution. The main contribution of this paper is the development of a methodology that optimizes the results of any clustering algorithm, regardless of data spatial distribution. The proposal is to evaluate the internal correlation of each cluster to proceed or not in a new partitioning round. In summary, the traditional validation indexes will continue to be used in the cluster’s partition process, but it is the internal correlation measure of each one that will define the stopping splitting criteria. This approach was tested in a real waveforms database using the K-means algorithm with the Silhouette and also the Davies–Bouldin validation indexes. The results were compared with a specific methodology for that database and were shown to be totally consistent. Full article
(This article belongs to the Special Issue Big Data and Advanced Analytics in Energy Systems and Applications)
►▼ Show Figures

Figure 1

14 pages, 4003 KiB  
Article
Chiller Optimization Using Data Mining Based on Prediction Model, Clustering and Association Rule Mining
by Elsa Chaerun Nisa, Yean-Der Kuan and Chin-Chang Lai
Energies 2021, 14(20), 6494; https://doi.org/10.3390/en14206494 - 11 Oct 2021
Cited by 4 | Viewed by 2372
Abstract
The chiller is the major energy consuming HVAC component in a building. Currently, huge chiller data is easy to obtain due to Internet of Things (IoT) technology development. In order to optimize the chiller system, this study presents a data mining technique that [...] Read more.
The chiller is the major energy consuming HVAC component in a building. Currently, huge chiller data is easy to obtain due to Internet of Things (IoT) technology development. In order to optimize the chiller system, this study presents a data mining technique that utilizes the available chiller data. The data mining techniques used are prediction model, clustering analysis, and association rules mining (ARM) analysis. The dataset was collected every minute for a year from a water-cooled chiller at an institutional building in Taiwan and from meteorological data. The power consumption prediction model was built using deep neural networks with 0.955 of R2, 4.470 of MAE, and 6.716 of RMSE. Clustering analysis was performed using the k-means algorithm and ARM analysis was performed using Apriori algorithm. Each cluster identifies those operational parameters that have strong association rules with high performance. The operational parameters from ARM were simulated using the prediction model. The simulation result shows that the ARM operational parameters can successfully save the energy consumption by 22.36 MWh or 18.17% in a year. Full article
(This article belongs to the Special Issue Big Data and Advanced Analytics in Energy Systems and Applications)
►▼ Show Figures

Figure 1

19 pages, 3704 KiB  
Article
Cyber Risks to Critical Smart Grid Assets of Industrial Control Systems
by Chenyang Liu, Yazeed Alrowaili, Neetesh Saxena and Charalambos Konstantinou
Energies 2021, 14(17), 5501; https://doi.org/10.3390/en14175501 - 03 Sep 2021
Cited by 9 | Viewed by 2890
Abstract
Cybersecurity threats targeting industrial control systems (ICS) have significantly increased in the past years. Moreover, the need for users/operators to understand the consequences of attacks targeting these systems and protect all assets is vital. This work explores asset discovery in ICS and how [...] Read more.
Cybersecurity threats targeting industrial control systems (ICS) have significantly increased in the past years. Moreover, the need for users/operators to understand the consequences of attacks targeting these systems and protect all assets is vital. This work explores asset discovery in ICS and how to rank these assets based on their criticality. This paper also discusses asset discovery and its components. We further present existing solutions and tools for asset discovery. We implement a method to identify critical assets based on their connection and discuss related results and evaluation. The evaluation utilises four attack scenarios to stress the importance of protecting these critical assets since the failure to protect them can lead to serious consequences. Using a 12-bus system case, our results show that targeting such a system can increase and overload transmission lines values to 120% and 181% MVA, which can affect the power supply and disrupt service, and it can increase the cost up to 60%, affecting the productivity of this electric grid. Full article
(This article belongs to the Special Issue Big Data and Advanced Analytics in Energy Systems and Applications)
►▼ Show Figures

Figure 1

Show export options expand_more Show export options expand_less
Select all
Export citation of selected articles as:
 
Displaying articles 1-15
Back to TopTop