Special Issues
Table of Content

Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems

Submission Deadline: 30 September 2025 (closed) View: 24954 Submit to Journal

Guest Editors

Prof. Dr. Wei-Chiang Hong, Harbin Engineering University, China

Assco. Prof. Dr. Yi Liang, Hebei GEO University, China


Summary

Diverse energy and power systems have been playing a significantly critical role in the revolution of sustainable energy supply for the future, such as gas turbines, wind turbines, photovoltaic panels, building heating, ventilation and air-conditioning (HVAC) systems, etc., which have a great impact on energy resources and efficiencies. Due to the emerging artificial intelligence and machine learning, traditional modeling techniques in these energy systems have met challenges in still leveraging physics model and first principle-based approaches. Moreover, with the rapid development of hardware and computing techniques, new modeling approaches for energy systems have become more and more important for system design, integration, analysis, control, and management. This Special Issue aims to present and disseminate the most recent advances related to modeling theory, approaches, and applications of energy systems.

 

Topic of interests for publication include but are not limited to:

• Energy demand forecasting and management

• Optimization of renewable energy systems

• Predictive maintenance of energy equipment & systems

• Prediction and prevention applied to energy system

• Analysis and interpretation of energy data for decision making purposes

• Intelligent energy management systems

• Deep Learning for Predictive Maintenance in Renewable Energy Systems

• Artificial Neural Networks for Optimal Operation of Microgrid Systems

• Application of Deep Learning for Improving the Efficiency of Distributed Energy Resources


Keywords

• Artificial intelligence
• Machine learning
• Energy systems
• Renewable energy
• Demand & forecasting
• Energy generation, transmission, and distribution
• Energy data analysis
• Energy management

Published Papers


  • Open Access

    REVIEW

    A Review of Modern Strategies for Enhancing Power Quality and Hosting Capacity in Renewable-Integrated Grids: From Conventional Devices to AI-Based Solutions

    Adel A.Abou El-Ela, Ragab A. El-Sehiemy, Abdallah Nazih, Asmaa A. Mubarak, Eman S. Ali
    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1349-1388, 2025, DOI:10.32604/cmes.2025.069507
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract Distribution systems face significant challenges in maintaining power quality issues and maximizing renewable energy hosting capacity due to the increased level of photovoltaic (PV) systems integration associated with varying loading and climate conditions. This paper provides a comprehensive overview on the exit strategies to enhance distribution system operation, with a focus on harmonic mitigation, voltage regulation, power factor correction, and optimization techniques. The impact of passive and active filters, custom power devices such as dynamic voltage restorers (DVRs) and static synchronous compensators (STATCOMs), and grid modernization technologies on power quality is examined. Additionally, this paper… More >

  • Open Access

    ARTICLE

    Efficient Time-Series Feature Extraction and Ensemble Learning for Appliance Categorization Using Smart Meter Data

    Ugur Madran, Saeed Mian Qaisar, Duygu Soyoglu
    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1969-1992, 2025, DOI:10.32604/cmes.2025.072024
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract Recent advancements in smart-meter technology are transforming traditional power systems into intelligent smart grids. It offers substantial benefits across social, environmental, and economic dimensions. To effectively realize these advantages, a fine-grained collection and analysis of smart meter data is essential. However, the high dimensionality and volume of such time-series present significant challenges, including increased computational load, data transmission overhead, latency, and complexity in real-time analysis. This study proposes a novel, computationally efficient framework for feature extraction and selection tailored to smart meter time-series data. The approach begins with an extensive offline analysis, where features are… More >

  • Open Access

    ARTICLE

    IoT Based Transmission Line Fault Classification Using Regularized RBF-ELM and Virtual PMU in a Smart Grid

    Kunjabihari Swain, Murthy Cherukuri, Indu Sekhar Samanta, Bhargav Appasani, Nicu Bizon, Mihai Oproescu
    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.2, pp. 1993-2015, 2025, DOI:10.32604/cmes.2025.067121
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract Transmission line faults pose a significant threat to power system resilience, underscoring the need for accurate and rapid fault identification to facilitate proper resource monitoring, economic loss prevention, and blackout avoidance. Extreme learning machine (ELM) offers a compelling solution for rapid classification, achieving network training in a single epoch. Leveraging the Internet of Things (IoT) and the virtual instrumentation capabilities of LabVIEW, ELM can enable the swift and precise identification of transmission line faults. This paper presents a regularized radial basis function (RBF) ELM-based fault detection and classification system for transmission lines, utilizing a LabVIEW More >

  • Open Access

    ARTICLE

    A Flexible Decision Method for Holonic Smart Grids

    Ihab Taleb, Guillaume Guerard, Frédéric Fauberteau, Nga Nguyen, Pascal Clain
    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 597-619, 2025, DOI:10.32604/cmes.2025.070517
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract Isolated power systems, such as those on islands, face acute challenges in balancing energy demand with limited generation resources, making them particularly vulnerable to disruptions. This paper addresses these challenges by proposing a novel control and simulation framework based on a holonic multi-agent architecture, specifically developed as a digital twin for the Mayotte island grid. The primary contribution is a multi-objective optimization model, driven by a genetic algorithm, designed to enhance grid resilience through intelligent, decentralized decision-making. The efficacy of this architecture is validated through three distinct simulation scenarios: (1) a baseline scenario establishing nominal… More >

  • Open Access

    ARTICLE

    Extending DDPG with Physics-Informed Constraints for Energy-Efficient Robotic Control

    Abubakar Elsafi, Arafat Abdulgader Mohammed Elhag, Lubna A. Gabralla, Ali Ahmed, Ashraf Osman Ibrahim
    CMES-Computer Modeling in Engineering & Sciences, Vol.145, No.1, pp. 621-647, 2025, DOI:10.32604/cmes.2025.072726
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract Energy efficiency stands as an essential factor when implementing deep reinforcement learning (DRL) policies for robotic control systems. Standard algorithms, including Deep Deterministic Policy Gradient (DDPG), primarily optimize task rewards but at the cost of excessively high energy consumption, making them impractical for real-world robotic systems. To address this limitation, we propose Physics-Informed DDPG (PI-DDPG), which integrates physics-based energy penalties to develop energy-efficient yet high-performing control policies. The proposed method introduces adaptive physics-informed constraints through a dynamic weighting factor (), enabling policies that balance reward maximization with energy savings. Our motivation is to overcome the… More >

  • Open Access

    ARTICLE

    A Hybrid LSTM-Single Candidate Optimizer Model for Short-Term Wind Power Prediction

    Mehmet Balci, Emrah Dokur, Ugur Yuzgec
    CMES-Computer Modeling in Engineering & Sciences, Vol.144, No.1, pp. 945-968, 2025, DOI:10.32604/cmes.2025.067851
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract Accurate prediction of wind energy plays a vital role in maintaining grid stability and supporting the broader shift toward renewable energy systems. Nevertheless, the inherently variable nature of wind and the intricacy of high-dimensional datasets pose major obstacles to reliable forecasting. To address these difficulties, this study presents an innovative hybrid method for short-term wind power prediction by combining a Long Short-Term Memory (LSTM) network with a Single Candidate Optimizer (SCO) algorithm. In contrast to conventional techniques that rely on random parameter initialization, the proposed LSTM-SCO framework leverages the distinctive capability of SCO to work More >

    Graphic Abstract

    A Hybrid LSTM-Single Candidate Optimizer Model for Short-Term Wind Power Prediction

  • Open Access

    ARTICLE

    Predicting Short-Term Wind Power Generation at Musalpetti Wind Farm: Model Development and Analysis

    Namal Rathnayake, Jeevani Jayasinghe, Rashmi Semasinghe, Upaka Rathnayake
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2287-2305, 2025, DOI:10.32604/cmes.2025.064464
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract In this study, a machine learning-based predictive model was developed for the Musa petti Wind Farm in Sri Lanka to address the need for localized forecasting solutions. Using data on wind speed, air temperature, nacelle position, and actual power, lagged features were generated to capture temporal dependencies. Among 24 evaluated models, the ensemble bagging approach achieved the best performance, with R2 values of 0.89 at 0 min and 0.75 at 60 min. Shapley Additive exPlanations (SHAP) analysis revealed that while wind speed is the primary driver for short-term predictions, air temperature and nacelle position become more More >

  • Open Access

    ARTICLE

    Enhanced Fault Detection and Diagnosis in Photovoltaic Arrays Using a Hybrid NCA-CNN Model

    Umit Cigdem Turhal, Yasemin Onal, Kutalmis Turhal
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.2, pp. 2307-2332, 2025, DOI:10.32604/cmes.2025.064269
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract The reliability and efficiency of photovoltaic (PV) systems are essential for sustainable energy production, requiring accurate fault detection to minimize energy losses. This study proposes a hybrid model integrating Neighborhood Components Analysis (NCA) with a Convolutional Neural Network (CNN) to improve fault detection and diagnosis. Unlike Principal Component Analysis (PCA), which may compromise class relationships during feature extraction, NCA preserves these relationships, enhancing classification performance. The hybrid model combines NCA with CNN, a fundamental deep learning architecture, to enhance fault detection and diagnosis capabilities. The performance of the proposed NCA-CNN model was evaluated against other More >

    Graphic Abstract

    Enhanced Fault Detection and Diagnosis in Photovoltaic Arrays Using a Hybrid NCA-CNN Model

  • Open Access

    ARTICLE

    Prediction and Comparative Analysis of Rooftop PV Solar Energy Efficiency Considering Indoor and Outdoor Parameters under Real Climate Conditions Factors with Machine Learning Model

    Gökhan Şahin, Ihsan Levent, Gültekin Işık, Wilfried van Sark, Sabir Rustemli
    CMES-Computer Modeling in Engineering & Sciences, Vol.143, No.1, pp. 1215-1248, 2025, DOI:10.32604/cmes.2025.063193
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract This research investigates the influence of indoor and outdoor factors on photovoltaic (PV) power generation at Utrecht University to accurately predict PV system performance by identifying critical impact factors and improving renewable energy efficiency. To predict plant efficiency, nineteen variables are analyzed, consisting of nine indoor photovoltaic panel characteristics (Open Circuit Voltage (Voc), Short Circuit Current (Isc), Maximum Power (Pmpp), Maximum Voltage (Umpp), Maximum Current (Impp), Filling Factor (FF), Parallel Resistance (Rp), Series Resistance (Rs), Module Temperature) and ten environmental factors (Air Temperature, Air Humidity, Dew Point, Air Pressure, Irradiation, Irradiation Propagation, Wind Speed, Wind… More >

  • Open Access

    ARTICLE

    Enhancing Safety in Electric Vehicles: Multi-Tiered Fault Detection for Micro Short Circuits and Aging in Battery Modules

    Yi-Feng Luo, Jyuan-Fong Yen, Wen-Cheng Su
    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.3, pp. 3069-3087, 2025, DOI:10.32604/cmes.2025.061180
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract This article proposes a multi-tiered fault detection system for series-connected lithium-ion battery modules. Improper use of batteries can lead to electrolyte decomposition, resulting in the formation of lithium dendrites. These dendrites may pierce the separator, leading to the failure of the insulation layer between electrodes and causing micro short circuits. When a micro short circuit occurs, the electrolyte typically undergoes exothermic reactions, leading to thermal runaway and posing a safety risk to users. Relying solely on temperature-based judgment mechanisms within the battery management system often results in delayed intervention. To address this issue, the article More >

  • Open Access

    ARTICLE

    Stability Prediction in Smart Grid Using PSO Optimized XGBoost Algorithm with Dynamic Inertia Weight Updation

    Adel Binbusayyis, Mohemmed Sha
    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 909-931, 2025, DOI:10.32604/cmes.2024.058202
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract Prediction of stability in SG (Smart Grid) is essential in maintaining consistency and reliability of power supply in grid infrastructure. Analyzing the fluctuations in power generation and consumption patterns of smart cities assists in effectively managing continuous power supply in the grid. It also possesses a better impact on averting overloading and permitting effective energy storage. Even though many traditional techniques have predicted the consumption rate for preserving stability, enhancement is required in prediction measures with minimized loss. To overcome the complications in existing studies, this paper intends to predict stability from the smart grid… More >

  • Open Access

    ARTICLE

    Multi-Step Clustering of Smart Meters Time Series: Application to Demand Flexibility Characterization of SME Customers

    Santiago Bañales, Raquel Dormido, Natividad Duro
    CMES-Computer Modeling in Engineering & Sciences, Vol.142, No.1, pp. 869-907, 2025, DOI:10.32604/cmes.2024.054946
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract Customer segmentation according to load-shape profiles using smart meter data is an increasingly important application to vital the planning and operation of energy systems and to enable citizens’ participation in the energy transition. This study proposes an innovative multi-step clustering procedure to segment customers based on load-shape patterns at the daily and intra-daily time horizons. Smart meter data is split between daily and hourly normalized time series to assess monthly, weekly, daily, and hourly seasonality patterns separately. The dimensionality reduction implicit in the splitting allows a direct approach to clustering raw daily energy time series… More >

    Graphic Abstract

    Multi-Step Clustering of Smart Meters Time Series: Application to Demand Flexibility Characterization of SME Customers

  • Open Access

    ARTICLE

    Intelligent Fractional-Order Controller for SMES Systems in Renewable Energy-Based Microgrid

    Aadel M. Alatwi, Abualkasim Bakeer, Sherif A. Zaid, Ibrahem E. Atawi, Hani Albalawi, Ahmed M. Kassem
    CMES-Computer Modeling in Engineering & Sciences, Vol.140, No.2, pp. 1807-1830, 2024, DOI:10.32604/cmes.2024.048521
    (This article belongs to the Special Issue: Advanced Artificial Intelligence and Machine Learning Methods Applied to Energy Systems)
    Abstract An autonomous microgrid that runs on renewable energy sources is presented in this article. It has a superconducting magnetic energy storage (SMES) device, wind energy-producing devices, and an energy storage battery. However, because such microgrids are nonlinear and the energy they create varies with time, controlling and managing the energy inside them is a difficult issue. Fractional-order proportional integral (FOPI) controller is recommended for the current research to enhance a standalone microgrid’s energy management and performance. The suggested dedicated control for the SMES comprises two loops: the outer loop, which uses the FOPI to regulate… More >

Share Link