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Algorithms
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Advanced Artificial Intelligence/Machine Learning Techniques for Safe Operation and Control...
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Advanced Artificial Intelligence/Machine Learning Techniques for Safe Operation and Control in Power and Sustainable Energy Systems

A special issue of Algorithms (ISSN 1999-4893). This special issue belongs to the section "Algorithms for Multidisciplinary Applications".

Deadline for manuscript submissions: 30 June 2025 | Viewed by 1937

Special Issue Editors

Dr. Van-Hai Bui

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, USA
Interests: energy management systems; applications of machine learning in smart grids; operation and control of power and energy systems
Special Issues, Collections and Topics in MDPI journals
Dr. Xuan Zhou

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, Dearborn, MI 48126, USA
Interests: battery design and manufacturing; battery modelling and control for electric vehicles
Special Issues, Collections and Topics in MDPI journals
Dr. Wencong Su

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, USA
Interests: power systems; electrified transportation systems; cyber–physical systems
Special Issues, Collections and Topics in MDPI journals
Dr. Akhtar Hussain

E-Mail Website
Guest Editor
Department of Electrical and Computer Engineering, Laval University, 2325 Rue de l'Université, Québec, QC G1V 0A6, Canada
Interests: power system automation; smart grids; microgrid operation
Special Issues, Collections and Topics in MDPI journals

Special Issue Information

Dear Colleagues,

The increasing integration of distributed energy resources (DERs) into power distribution networks introduces numerous sources of uncertainty, significantly challenging the operation and control of power systems. These challenges may include grid stability, security risks, frequency instability, and voltage fluctuations. Conventional optimization methods often falter in handling such uncertainty, leading to increased operational costs and decreased service reliability. Recently, the rapid development of artificial intelligence/machine learning, especially deep reinforcement learning, has offered promising sustainable solutions for managing power system operations amidst these uncertainties. A key limitation of conventional deep reinforcement learning approaches, however, is their inability to ensure safety constraints during system operations, potentially resulting in electrical system instability or equipment failures.

Therefore, the safe operation of critical infrastructure, such as power and energy systems, has been attracting significant attention from the academic and industrial research communities. Integrating safety considerations into AI/ML is crucial for ensuring reliability, security, and efficiency across the generation, transmission, and distribution of electricity.

In this Special Issue, original research articles and reviews are welcome. Research areas may include (but are not limited to) the following:

  • Advanced machine learning for power and energy systems;
  • Energy management system implementation;
  • Explainable AI (XAI) applications;
  • Human-in-the-loop ML applications;
  • Multiagent system-based management systems;
  • Sustainable energy systems;
  • Safe reinforcement learning in power system operation and control;
  • Uncertainty mitigation with extensive DER integration.
  • We look forward to receiving your contributions.

You may choose our Joint Special Issue in Sustainability

Dr. Van-Hai Bui
Dr. Xuan Zhou
Dr. Wencong Su
Dr. Akhtar Hussain
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. Algorithms is an international peer-reviewed open access monthly 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 1600 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

  • the applications of advanced machine learning in sustainable energy systems
  • energy management systems
  • microgrids
  • power system operation and control
  • reinforcement learning

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Further information on MDPI's Special Issue polices can be found here.

Published Papers (3 papers)

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Research

19 pages, 5212 KiB  
Article
Assessment of Solar Energy Generation Toward Net-Zero Energy Buildings
by Rayan Khalil, Guilherme Vieira Hollweg, Akhtar Hussain, Wencong Su and Van-Hai Bui
Algorithms 2024, 17(11), 528; https://doi.org/10.3390/a17110528 - 16 Nov 2024
Viewed by 326
Abstract
With the continuous rise in the energy consumption of buildings, the study and integration of net-zero energy buildings (NZEBs) are essential for mitigating the harmful effects associated with this trend. However, developing an energy management system for such buildings is challenging due to [...] Read more.
With the continuous rise in the energy consumption of buildings, the study and integration of net-zero energy buildings (NZEBs) are essential for mitigating the harmful effects associated with this trend. However, developing an energy management system for such buildings is challenging due to uncertainties surrounding NZEBs. This paper introduces an optimization framework comprising two major stages: (i) renewable energy prediction and (ii) multi-objective optimization. A prediction model is developed to accurately forecast photovoltaic (PV) system output, while a multi-objective optimization model is designed to identify the most efficient ways to produce cooling, heating, and electricity at minimal operational costs. These two stages not only help mitigate uncertainties in NZEBs but also reduce dependence on imported power from the utility grid. Finally, to facilitate the deployment of the proposed framework, a graphical user interface (GUI) has been developed, providing a user-friendly environment for building operators to determine optimal scheduling and oversee the entire system. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence/Machine Learning Techniques for Safe Operation and Control in Power and Sustainable Energy Systems)
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28 pages, 4502 KiB  
Article
Improved Bacterial Foraging Optimization Algorithm with Machine Learning-Driven Short-Term Electricity Load Forecasting: A Case Study in Peninsular Malaysia
by Farah Anishah Zaini, Mohamad Fani Sulaima, Intan Azmira Wan Abdul Razak, Mohammad Lutfi Othman and Hazlie Mokhlis
Algorithms 2024, 17(11), 510; https://doi.org/10.3390/a17110510 - 6 Nov 2024
Viewed by 431
Abstract
Accurate electricity demand forecasting is crucial for ensuring the sustainability and reliability of power systems. Least square support vector machines (LSSVM) are well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in [...] Read more.
Accurate electricity demand forecasting is crucial for ensuring the sustainability and reliability of power systems. Least square support vector machines (LSSVM) are well suited to handle complex non-linear power load series. However, the less optimal regularization parameter and the Gaussian kernel function in the LSSVM model have contributed to flawed forecasting accuracy and random generalization ability. Thus, these parameters of LSSVM need to be chosen appropriately using intelligent optimization algorithms. This study proposes a new hybrid model based on the LSSVM optimized by the improved bacterial foraging optimization algorithm (IBFOA) for forecasting the short-term daily electricity load in Peninsular Malaysia. The IBFOA based on the sine cosine equation addresses the limitations of fixed chemotaxis constants in the original bacterial foraging optimization algorithm (BFOA), enhancing its exploration and exploitation capabilities. Finally, the load forecasting model based on LSSVM-IBFOA is constructed using mean absolute percentage error (MAPE) as the objective function. The comparative analysis demonstrates the model, achieving the highest determination coefficient (R2) of 0.9880 and significantly reducing the average MAPE value by 28.36%, 27.72%, and 5.47% compared to the deep neural network (DNN), LSSVM, and LSSVM-BFOA, respectively. Additionally, IBFOA exhibits faster convergence times compared to BFOA, highlighting the practicality of LSSVM-IBFOA for short-term load forecasting. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence/Machine Learning Techniques for Safe Operation and Control in Power and Sustainable Energy Systems)
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25 pages, 5210 KiB  
Article
Application of SHAP and Multi-Agent Approach for Short-Term Forecast of Power Consumption of Gas Industry Enterprises
by Alina I. Stepanova, Alexandra I. Khalyasmaa, Pavel V. Matrenin and Stanislav A. Eroshenko
Algorithms 2024, 17(10), 447; https://doi.org/10.3390/a17100447 - 8 Oct 2024
Viewed by 862
Abstract
Currently, machine learning methods are widely applied in the power industry to solve various tasks, including short-term power consumption forecasting. However, the lack of interpretability of machine learning methods can lead to their incorrect use, potentially resulting in electrical system instability or equipment [...] Read more.
Currently, machine learning methods are widely applied in the power industry to solve various tasks, including short-term power consumption forecasting. However, the lack of interpretability of machine learning methods can lead to their incorrect use, potentially resulting in electrical system instability or equipment failures. This article addresses the task of short-term power consumption forecasting, one of the tasks of enhancing the energy efficiency of gas industry enterprises. In order to reduce the risks of making incorrect decisions based on the results of short-term power consumption forecasts made by machine learning methods, the SHapley Additive exPlanations method was proposed. Additionally, the application of a multi-agent approach for the decomposition of production processes using self-generation agents, energy storage agents, and consumption agents was demonstrated. It can enable the safe operation of critical infrastructure, for instance, adjusting the operation modes of self-generation units and energy-storage systems, optimizing the power consumption schedule, and reducing electricity and power costs. A comparative analysis of various algorithms for constructing decision tree ensembles was conducted to forecast power consumption by gas industry enterprises with different numbers of categorical features. The experiments demonstrated that using the developed method and production process factors reduced the MAE from 105.00 kWh (MAPE of 16.81%), obtained through expert forecasting, to 15.52 kWh (3.44%). Examples were provided of how the use of SHapley Additive exPlanation can increase the safety of the electrical system management of gas industry enterprises by improving experts’ confidence in the results of the information system. Full article
(This article belongs to the Special Issue Advanced Artificial Intelligence/Machine Learning Techniques for Safe Operation and Control in Power and Sustainable Energy Systems)
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