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Artificial Intelligence Applications to Design and Modeling of Energy Systems
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Artificial Intelligence Applications to Design and Modeling of 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 (31 December 2023) | Viewed by 5167

Special Issue Editors

Dr. Muhammad Sulaiman

E-Mail Website
Guest Editor
Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Pakistan
Interests: Machine learning; Optimization; Fluid mechanics;
Prof. Dr. Abdellah Salhi

E-Mail Website
Guest Editor
Department of Mathematical Sciences, University of Essex, Wivenhoe Park, Colchester CO4 3SQ, UK
Interests: Optimisation Mathematical Programming and Heuristics (Evolutionary Computing, Nature-inspired Algorithms, the Strawberry Algorithm)
Dr. Ashfaq Ahmad

E-Mail Website
Guest Editor
Department of Mathematics, Abdul Wali Khan University, Mardan 23200, Pakistann
Interests: Differential Equations, Soft Computing, Optimization Problems, Mathematical Modeling, Energy Systems, Metaheuristic Techniques, Evolutionary Computation.

Special Issue Information

Dear Colleagues,

We are pleased to announce this Special Issue on the Artificial Intelligence Applications to Design and Modeling of Energy Systems. This issue aims to bring together research that addresses pressing societal issues related to modeling energy systems, sustainability, and climate change using AI-based mathematical techniques, for example, by improving the efficiency of energy systems and making better use of renewable energy and storage. We welcome research using a range of AI-based computational techniques, including machine learning and multi-agent systems, as well as research on the interaction between humans and intelligent energy systems. Topics include, but are not limited to, the following:

  • Machine learning;
  • Multi-agent systems;
  • Decentralized optimization;
  • Decision making under uncertainty;
  • Community energy markets;
  • Storage and renewable energy;
  • Mechanism design and incentive engineering;
  • Electric vehicles;
  • Human–system interaction;
  • Smart energy systems;
  • Artificial neural networks and energy systems;
  • Mathematical modeling of energy systems.

Dr. Muhammad Sulaiman
Prof. Dr. Abdellah Salhi
Dr. Ashfaq Ahmad
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

  • mathematical modeling
  • machine learning
  • artificial neural networks
  • energy systems

Published Papers (2 papers)

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Research

22 pages, 2198 KiB  
Article
Instantaneous Electricity Peak Load Forecasting Using Optimization and Machine Learning
by Mustafa Saglam, Xiaojing Lv, Catalina Spataru and Omer Ali Karaman
Energies 2024, 17(4), 777; https://doi.org/10.3390/en17040777 - 6 Feb 2024
Viewed by 1877
Abstract
Accurate instantaneous electricity peak load prediction is crucial for efficient capacity planning and cost-effective electricity network establishment. This paper aims to enhance the accuracy of instantaneous peak load forecasting by employing models incorporating various optimization and machine learning (ML) methods. This study examines [...] Read more.
Accurate instantaneous electricity peak load prediction is crucial for efficient capacity planning and cost-effective electricity network establishment. This paper aims to enhance the accuracy of instantaneous peak load forecasting by employing models incorporating various optimization and machine learning (ML) methods. This study examines the impact of independent inputs on peak load estimation through various combinations and subsets using multilinear regression (MLR) equations. This research utilizes input data from 1980 to 2020, including import and export data, population, and gross domestic product (GDP), to forecast the instantaneous electricity peak load as the output value. The effectiveness of these techniques is evaluated based on error metrics, including mean absolute error (MAE), mean square error (MSE), mean absolute percentage error (MAPE), root mean square error (RMSE), and R2. The comparison extends to popular optimization methods, such as particle swarm optimization (PSO), and the newest method in the field, including dandelion optimizer (DO) and gold rush optimizer (GRO). This comparison is made against conventional machine learning methods, such as support vector regression (SVR) and artificial neural network (ANN), in terms of their prediction accuracy. The findings indicate that the ANN and GRO approaches produce the least statistical errors. Furthermore, the correlation matrix indicates a robust positive linear correlation between GDP and instantaneous peak load. The proposed model demonstrates strong predictive capabilities for estimating peak load, with ANN and GRO performing exceptionally well compared to other methods. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications to Design and Modeling of Energy Systems)
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19 pages, 5288 KiB  
Article
A Forecasting Model of Wind Power Based on IPSO–LSTM and Classified Fusion
by Qiuhong Huang and Xiao Wang
Energies 2022, 15(15), 5531; https://doi.org/10.3390/en15155531 - 29 Jul 2022
Cited by 10 | Viewed by 1361
Abstract
To improve the predicting accuracy of wind power, this paper proposes a forecasting model of wind power based on the IPSO–LSTM model and classified fusion, which not only overcomes the shortcoming of the artificially determined parameters of LSTM, but also solves the problem [...] Read more.
To improve the predicting accuracy of wind power, this paper proposes a forecasting model of wind power based on the IPSO–LSTM model and classified fusion, which not only overcomes the shortcoming of the artificially determined parameters of LSTM, but also solves the problem that the fused accuracy may be reduced by the environment when adopting a single fusion model. Firstly, some wind speed sub-series were obtained by decomposing the original wind speed according to the wavelet packet decomposition (WPD), and the data sets formed by combining these sub-series with meteorological elements. Subsequently, the wind power components formed by wind speed decomposition are predicted through the long short-term memory neural network (LSTM), which is optimized by the improved particle swarm optimization (IPSO). Consequently, the predicting value of the final wind power was acquired by adopting the method of classified fusion to calculate the wind power components. Several case studies were carried out on the proposed model with the help of Python. It is found from those relevant results that the RMSE and MAE of the proposed model is 1.2382 and 0.8210, respectively. Moreover, the R2 is 0.9952. Those simulating results show that the proposed model may be better for fitting the actual curve of wind power and has excellent predicting accuracy. Full article
(This article belongs to the Special Issue Artificial Intelligence Applications to Design and Modeling of Energy Systems)
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