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Intelligent Energy Systems and Energy Policy

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

Deadline for manuscript submissions: closed (30 September 2021) | Viewed by 11799
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Special Issue Editors


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Guest Editor
AI Systems Lab, School of Nuclear Engineering, Purdue University, West Lafayette, IN 47907, USA
Interests: intelligent energy systems; future nuclear power; machine learning; big data

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Guest Editor
Department of Electrical and Computer Engineering, University of Thessaly, 382 21 Volos, Greece
Interests: formal models; multiagent systems; artificial intelligence applications for energy optimization; smart grids
Special Issues, Collections and Topics in MDPI journals

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Guest Editor
Department of Electrical and Computer Engineering, University of Texas at San Antonio (UTSA), San Antonio, TX 78249, USA
Interests: AI in radiation detection and nuclear security; AI in radiation sensor networks
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues, 

Smart energy is a wave of innovation that couples energy and power systems to information networks and that promises to revolutionize the way energy is utilized and to reshape fundamental notions, metrics, and goals of energy policy. 

For example, peak demand constrained power systems coupled with bandwidth constrained information networks can accommodate, through automation, a plethora of new planning, scheduling, control, and maintenance activities with measurable benefits that include but are not limited to enhanced energy security, greater energy intensity, externalized environmental costs, improved energy return on energy investement (EROI), and overall improvements in economic and societal impact over the lifecycle of energy infrastructures. 

Recently, we have come to recognize the role of Artificial Intelligence in managing sophisticated technologies and ensuring safety and efficiency as well as cybersecurity for critical facilities such as nuclear generators, where connectivity may open the door for undesirable entry.

Nevertheless, a gap has emerged between conventional energy systems and energy policy and the emerging possibilities of machine embedded intelligence. The science and technology required to address such problems come from many disciplines, including economics, informatics, complex netoworks, fuzzy systems, and neural and evolutionary computing. Hence, a diversity of approaches need to be considered to examine, develop, and integrate emerging and future research into a coherent theoretical framework that will be bring to bear AI, machine learning, and big data into Energy Policy.

In this Special Issue on Intelligent Energy Systems and Energy Policy, we will seek papers that bridge the gap between energy and AI systems.

Prof. Dr. Lefteri H. Tsoukalas
Prof. Dr. Aspassia Daskalopulu
Prof. Dr. Miltiadis (Miltos) Alamaniotis
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

  • Energy Policy 
  • Big Data 
  • Machine Learning 
  • Intelligent Energy Systems 
  • Energy Networks 
  • Neural Computing 
  • Smart Cities 
  • Fuzzy Systems

Published Papers (4 papers)

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Research

13 pages, 17401 KiB  
Article
Intelligent Room-Based Identification of Electricity Consumption with an Ensemble Learning Method in Smart Energy
by Vincent Le, Joshua Ramirez and Miltiadis Alamaniotis
Energies 2021, 14(20), 6717; https://doi.org/10.3390/en14206717 - 15 Oct 2021
Viewed by 952
Abstract
This paper frames itself in the realm of smart energy technologies that can be utilized to satisfy the electricity demand of consumers. In this environment, demand response programs and the intelligent management of energy consumption that are offered by utility providers will play [...] Read more.
This paper frames itself in the realm of smart energy technologies that can be utilized to satisfy the electricity demand of consumers. In this environment, demand response programs and the intelligent management of energy consumption that are offered by utility providers will play a significant role in implementing smart energy. One of the approaches to implementing smart energy is to analyze consumption data and provide targeted contracts to consumers based on their individual consumption characteristics. To that end, the identification of individual consumption features is important for suppliers and utilities. Given the complexity of smart home load profiles, an appliance-based identification is nearly impossible. In this paper, we propose a different approach by grouping appliances based on their rooms; thus, we provide a room-based identification of energy consumption. To this end, this paper presents and tests an intelligent consumption identification methodology, that can be implemented in the form of an ensemble of artificial intelligence tools. The ensemble, which comprises four convolutional neural networks (CNNs) and four k-nearest neighbor (KNN) algorithms, is fed with smart submeter data and outputs the identified type of room in a given dwelling. Results obtained from real-world data exhibit the superiority of the ensemble, with respect to accuracy, as compared with individual CNN and KNN models. Full article
(This article belongs to the Special Issue Intelligent Energy Systems and Energy Policy)
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32 pages, 19506 KiB  
Article
A Prosumer Model Based on Smart Home Energy Management and Forecasting Techniques
by Nikolaos Koltsaklis, Ioannis P. Panapakidis, David Pozo and Georgios C. Christoforidis
Energies 2021, 14(6), 1724; https://doi.org/10.3390/en14061724 - 19 Mar 2021
Cited by 21 | Viewed by 3283
Abstract
This work presents an optimization framework based on mixed-integer programming techniques for a smart home’s optimal energy management. In particular, through a cost-minimization objective function, the developed approach determines the optimal day-ahead energy scheduling of all load types that can be either inelastic [...] Read more.
This work presents an optimization framework based on mixed-integer programming techniques for a smart home’s optimal energy management. In particular, through a cost-minimization objective function, the developed approach determines the optimal day-ahead energy scheduling of all load types that can be either inelastic or can take part in demand response programs and the charging/discharging programs of an electric vehicle and energy storage. The underlying energy system can also interact with the power grid, exchanging electricity through sales and purchases. The smart home’s energy system also incorporates renewable energy sources in the form of wind and solar power, which generate electrical energy that can be either directly consumed for the home’s requirements, directed to the batteries for charging needs (storage, electric vehicles), or sold back to the power grid for acquiring revenues. Three short-term forecasting processes are implemented for real-time prices, photovoltaics, and wind generation. The forecasting model is built on the hybrid combination of the K-medoids algorithm and Elman neural network. K-medoids performs clustering of the training set and is used for input selection. The forecasting is held via the neural network. The results indicate that different renewables’ availability highly influences the optimal demand allocation, renewables-based energy allocation, and the charging–discharging cycle of the energy storage and electric vehicle. Full article
(This article belongs to the Special Issue Intelligent Energy Systems and Energy Policy)
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18 pages, 4044 KiB  
Article
Fuzzy Control System for Smart Energy Management in Residential Buildings Based on Environmental Data
by Dimitrios Kontogiannis, Dimitrios Bargiotas and Aspassia Daskalopulu
Energies 2021, 14(3), 752; https://doi.org/10.3390/en14030752 - 1 Feb 2021
Cited by 27 | Viewed by 3759
Abstract
Modern energy automation solutions and demand response applications rely on load profiles to monitor and manage electricity consumption effectively. The introduction of smart control systems capable of handling additional fuzzy parameters, such as weather data, through machine learning methods, offers valuable insights in [...] Read more.
Modern energy automation solutions and demand response applications rely on load profiles to monitor and manage electricity consumption effectively. The introduction of smart control systems capable of handling additional fuzzy parameters, such as weather data, through machine learning methods, offers valuable insights in an attempt to adjust consumer behavior optimally. Following recent advances in the field of fuzzy control, this study presents the design and implementation of a fuzzy control system that processes environmental data in order to recommend minimum energy consumption values for a residential building. This system follows the forward chaining Mamdani approach and uses decision tree linearization for rule generation. Additionally, a hybrid feature selector is implemented based on XGBoost and decision tree metrics for feature importance. The proposed structure discovers and generates a small set of fuzzy rules that highlights the energy consumption behavior of the building based on time-series data of past operation. The response of the fuzzy system based on sample input data is presented, and the evaluation of its performance shows that the rule base generation is derived with improved accuracy. In addition, an overall smaller set of rules is generated, and the computation is faster compared to the baseline decision tree configuration. Full article
(This article belongs to the Special Issue Intelligent Energy Systems and Energy Policy)
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21 pages, 3415 KiB  
Article
Robust Optimal Operation Strategy for a Hybrid Energy System Based on Gas-Fired Unit, Power-to-Gas Facility and Wind Power in Energy Markets
by Masoud Agabalaye-Rahvar, Amin Mansour-Saatloo, Mohammad Amin Mirzaei, Behnam Mohammadi-Ivatloo, Kazem Zare and Amjad Anvari-Moghaddam
Energies 2020, 13(22), 6131; https://doi.org/10.3390/en13226131 - 23 Nov 2020
Cited by 22 | Viewed by 2660
Abstract
Gas-fired power units (GFUs) are the best technology in recent years due to lower natural gas prices, higher energy transformation performance, and lower CO2 emission, as compared to the conventional power units (CPUs). A permanent storage facility called power-to-gas (P2G) technology can [...] Read more.
Gas-fired power units (GFUs) are the best technology in recent years due to lower natural gas prices, higher energy transformation performance, and lower CO2 emission, as compared to the conventional power units (CPUs). A permanent storage facility called power-to-gas (P2G) technology can provide adaptation of ever-increasing renewable energy sources (RESs) fluctuations in power system operations, as well as reduce dependency to buy natural gas from the gas network. High investment and utilization expenditures of state-of-the-art P2G technology do not lead to economically effective operation individually. Therefore, in the present paper, an integrated GFUs-P2G-wind power unit (WPU) system is proposed to determine its optimal bidding strategy in the day-ahead energy market. A robust optimization approach is also taken into account to accommodate the proposed bidding strategy within the electricity price uncertainty environment. This problem was studied by using a case study that included a P2G facility, GFU, and WPUs to investigate the effectiveness and capability of the proposed robust bidding strategy in the day-ahead energy market. Simulation results indicate that the obtained profit increase by introducing the integrated energy system, and the P2G facility has a significant effect on participating GFUs, which have gas-consumption limitations in order to achieve maximum profit. Moreover, as it can be said, the amount of purchased natural gas is decreased in the situations, which do not have any gas-consumption limitations. Furthermore, the proposed system’s operation in the robust environment provides more robustness against electricity price deviations, although it leads to lower profit. In addition, deploying P2G technology causes about 1% incrementation in the introduced system profit. Full article
(This article belongs to the Special Issue Intelligent Energy Systems and Energy Policy)
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