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Artificial Intelligence Approach for Modeling of Renewable 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: 30 June 2024 | Viewed by 7989

Special Issue Editor


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Guest Editor
Department of Mechanical and Electrical Engineering, University of Southern Denmark, 6400 Sønderborg, Denmark
Interests: machine learning; energy systems, analysis and optimization; economic energy research; power electronics and power conversion; thermodynamics

Special Issue Information

Dear Colleagues,

I am inviting you to submit a manuscript for consideration and possible publication in the Special Issue "Artificial Intelligence Approach for Modeling of Renewable Energy Systems" in Energies, an open access journal publishing relevant scientific research and studies published online monthly by MDPI. Since its launch in 2008, the journal has been indexed by the Science Citation Index Expanded, COMPENDEX, and other large databases.

The development of different energy structures, distributed energy system models, and user-active engagement capabilities promotes a rapid transition toward an energy system in which multiple energy carriers and systems synergistically interact. Achieving smart energy systems faces many challenges, necessitating novel intelligent and flexible tools. These challenges include the requirement for large data processing, professional competence, remote cooperation, and real-time monitoring for energy systems. However, due to the high volatility and unpredictability of renewable energy generation, renewable energy systems are becoming increasingly complicated. The analysis, scheduling, and control problems of future renewable energy systems will be difficult to resolve using standard model-based techniques. Electricity system operators have recently deployed smart meters and other cutting-edge sensing equipment to gather a growing number of data. This advocates the use of artificial intelligence (AI) approaches that directly learn pertinent information from a vast number of data to manage complex nonlinear problems without making assumptions or simplifying them. This Special Issue aims to demonstrate the operation of expert systems and neural networks by presenting a variety of problems in the different domains of energy engineering. This Special Issue covers the use of AI in the green transition, as well as following topics: 

  • AI for energy system optimization: electricity network, district heating network, district cooling network;
  • AI for renewable energy generation forecasting: solar, wind, wave and tidal, hydropower, etc.;
  • Reinforcement learning for power and energy systems;
  • Signal processing and fault detection;
  • AI for energy system integration. 

Dr. Ali Khosravi
Guest Editor

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.

Published Papers (2 papers)

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Research

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17 pages, 1410 KiB  
Article
Local Interpretable Explanations of Energy System Designs
by Jonas Hülsmann, Julia Barbosa and Florian Steinke
Energies 2023, 16(5), 2161; https://doi.org/10.3390/en16052161 - 23 Feb 2023
Cited by 1 | Viewed by 1182
Abstract
Optimization-based design tools for energy systems often require a large set of parameter assumptions, e.g., about technology efficiencies and costs or the temporal availability of variable renewable energies. Understanding the influence of all these parameters on the computed energy system design via direct [...] Read more.
Optimization-based design tools for energy systems often require a large set of parameter assumptions, e.g., about technology efficiencies and costs or the temporal availability of variable renewable energies. Understanding the influence of all these parameters on the computed energy system design via direct sensitivity analysis is not easy for human decision-makers, since they may become overloaded by the multitude of possible results. We thus propose transferring an approach from explaining complex neural networks, so-called locally interpretable model-agnostic explanations (LIME), to this related problem. Specifically, we use variations of a small number of interpretable, high-level parameter features and sparse linear regression to obtain the most important local explanations for a selected design quantity. For a small bottom-up optimization model of a grid-connected building with photovoltaics, we derive intuitive explanations for the optimal battery capacity in terms of different cloud characteristics. For a larger application, namely a national model of the German energy transition until 2050, we relate path dependencies of the electrification of the heating and transport sector to the correlation measures between renewables and thermal loads. Compared to direct sensitivity analysis, the derived explanations are more compact and robust and thus more interpretable for human decision-makers. Full article
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Review

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27 pages, 5961 KiB  
Review
State-of-the-Art Research on Wireless Charging of Electric Vehicles Using Solar Energy
by Seyed Ali Kashani, Alireza Soleimani, Ali Khosravi and Mojtaba Mirsalim
Energies 2023, 16(1), 282; https://doi.org/10.3390/en16010282 - 27 Dec 2022
Cited by 17 | Viewed by 6366
Abstract
Within the past decade, since impediments in nonrenewable fuel sources and the contamination they cause, utilizing green energies, such as those that are sun-oriented, in tandem with electric vehicles, is a developing slant. Coordinating electric vehicle (EV) charging stations with sun-powered boards (PV) [...] Read more.
Within the past decade, since impediments in nonrenewable fuel sources and the contamination they cause, utilizing green energies, such as those that are sun-oriented, in tandem with electric vehicles, is a developing slant. Coordinating electric vehicle (EV) charging stations with sun-powered boards (PV) reduces the burden of EV charging on the control framework. This paper presents a state-of-the-art literature review on remote control transmission frameworks for charging the batteries of electric vehicles utilizing sun-based boards as a source of power generation. The goal of this research is to advance knowledge in the wireless power transfer (WPT) framework and explore more about solar-powered electric vehicle charging stations. To do this, a variety of solar-powered electric vehicle charging station types are thoroughly studied. Following a study of many framework elements, the types of WPT components are explored in a different section. Within the wireless power transmission framework for solar-powered electric vehicle charging, compensators and various coil structures are also investigated, along with the advantages of each coil over the others. This study also discusses the use of artificial intelligence (AI) in WPT frameworks and highlights the important aspects of developing an AI model. Full article
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