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

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A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Energy Sustainability".

Deadline for manuscript submissions: 31 October 2026 | Viewed by 24973

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Special Issue Editors

Dr. Van-Hai Bui

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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. Wencong Su

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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
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Dr. Xuan Zhou

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Guest Editor

Department of Electrical and Computer Engineering, University of Michigan-Dearborn, 4901 Evergreen Rd, Dearborn, MI 48128, USA
Interests: energy storage system; battery cycling and second life application; battery design and manufacturing; battery modelling; sensing and control for electric vehicles
Special Issues, Collections and Topics in MDPI journals

Dr. Akhtar Hussain

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Department of Electrical and Computer Engineering, Laval University, 2325 Rue de l'Université, Québec, QC G1V 0A6, Canada
Interests: power system operation; distributed energy resource integration; resource allocatyion; microgrid control
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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 Algorithms

Dr. Van-Hai Bui
Dr. Wencong Su
Dr. Xuan Zhou
Dr. Akhtar Hussain
Guest Editors

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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 250 words) can be sent to the Editorial Office for assessment.

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. Sustainability 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 2400 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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Published Papers (12 papers)

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25 pages, 3132 KB

Open AccessArticle

Study on the Impact of Electrical Substitution Coefficient on Natural Gas Load Forecasting Under Deep Electrification Scenario for Sustainable Energy Systems

by Wei Zhao, Bilin Shao, Yan Cao, Ming Hou, Chunhui Liu, Huibin Zeng, Hongbin Dai and Ning Tian

Sustainability 2026, 18(7), 3318; https://doi.org/10.3390/su18073318 - 29 Mar 2026

Viewed by 511

Abstract

Against the backdrop of the global energy transition toward deep electrification, the natural gas industry faces challenges, including increased load forecasting uncertainty and frequent extreme weather impacts. To enhance natural gas load forecasting accuracy and support system resilience planning, this study constructs a forecasting model based on quadratic decomposition and hybrid deep learning, incorporating an electricity substitution coefficient to characterize the coupling substitution effect between electricity and natural gas. Under the basic scenario, the VMD-WPD-TCN-BiGRU model is proposed. It employs variational mode decomposition and wavelet packet denoising for secondary signal denoising, combined with a time-series convolutional network and bidirectional gated recurrent unit to extract temporal features. Experiments demonstrate that, compared to mainstream methods such as CNN, BiLSTM, SVM, and XGBoost, this model achieves statistically significant reductions in MSE (11.11–96.21%), MAE (0.89–76.50%), and MAPE (4.10–67.94%), significantly improving forecasting accuracy. In the deep electrification scenario, the introduction of the electricity substitution coefficient further optimizes peak load forecasting for system pressure days under extreme low temperatures, elevating the overall R² to 0.9905 in the deep electrification scenario. Research indicates that the proposed model not only effectively improves the accuracy of short-term natural gas load forecasting but also provides quantitative support for enterprises to plan peak-shaving facilities, optimize pipeline networks, and respond to extreme weather emergencies in data silo environments. This contributes to strengthening the adaptability and long-term resilience of natural gas systems during the energy transition, thereby supporting the sustainable development of energy infrastructure. 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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19 pages, 2811 KB

Open AccessArticle

Optimizing EV Charging Infrastructure in Multi-Unit Residential Buildings for Sustainable Energy Management

by Abdulaziz Almutairi

Sustainability 2026, 18(6), 3051; https://doi.org/10.3390/su18063051 - 20 Mar 2026

Viewed by 418

Abstract

Inadequate charging infrastructure is considered a major challenge in the widespread adoption of electric vehicles (EVs), especially the absence of the optimal number of chargers in multi-unit residential buildings (MURBs) where several EVs need to share the same chargers. Therefore, this study proposes an optimization approach to determine the optimal number of chargers in MURBs, considering continuous and flexible charging options. First, the daily travel behavior of drivers is estimated using National Household Travel Survey (NHTS) data. Then, based on the technical parameters of EVs, the daily energy consumption of EVs is estimated. Subsequently, a mathematical problem with a unified objective function and scenario-specific constraints is developed. Finally, an index is proposed to quantify the unserved energy in EVs. Simulation results demonstrate the effectiveness of the flexible method in reducing the required number of chargers while ensuring satisfactory service. This research contributes to sustainable energy management, aligning with the United Nations’ Sustainable Development Goals (SDGs) for 2030. 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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34 pages, 4561 KB

Open AccessArticle

Comparative Forecasting of Electricity Load and Generation in Türkiye Using Prophet, XGBoost, and Deep Neural Networks

by Fuad Alhaj Omar and Nihat Pamuk

Sustainability 2026, 18(6), 2838; https://doi.org/10.3390/su18062838 - 13 Mar 2026

Viewed by 777

Abstract

Accurate electricity load forecasting has become increasingly challenging in Türkiye due to rapid structural changes in the power system driven by renewable energy expansion. Between 2016 and 2022, solar capacity increased by 130% and wind generation by 83%, resulting in renewable-induced variability exceeding 160%. To assess how different forecasting approaches respond to this evolving environment, Facebook Prophet, XGBoost, and Deep Neural Networks (DNNs) were evaluated using more than 55,000 hourly load observations under a strictly chronological out-of-sample validation framework. The comparative analysis reveals substantial differences in model performance. XGBoost achieved the highest forecasting accuracy, with a Mean Absolute Error of 981.48 MWh, a Root Mean Squared Error of 1344.15 MWh, and a Mean Absolute Percentage Error of 2.72%, while effectively capturing rapid intraday variations and maintaining peak deviations within ±1100 MWh. DNN models delivered competitive overall accuracy (MAE: 997.82 MWh; MAPE: 2.77%) but exhibited a tendency to smooth temporal variations, leading to an underestimation of extreme winter peaks by up to 4100 MWh. In contrast, Prophet showed limited adaptability to the observed structural volatility, producing errors nearly seven times higher than XGBoost (MAE: 7041.79 MWh; RMSE: 8718.14 MWh). Based on these findings, a layered forecasting framework is proposed, employing XGBoost for short-term operational dispatch and reserving statistical models for long-term planning and policy analysis. 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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21 pages, 2775 KB

Open AccessArticle

Deep Learning-Based Disaggregation of EV Fast Charging Stations for Intelligent Energy Management in Smart Grids

by Sami M. Alshareef

Sustainability 2026, 18(6), 2729; https://doi.org/10.3390/su18062729 - 11 Mar 2026

Viewed by 413

Abstract

This paper investigates the deployment of four electric vehicle (EV) fast-charging stations (FCSs) in a commercial facility’s parking area, where multiple service centers operate on varying schedules. The commercial load demand is modeled using Monte Carlo Simulation (MCS), introducing realistic stochastic variability and overlapping power patterns with FCS operations. A single-point sensing strategy at the point of common coupling (PCC) is adopted for load disaggregation. Continuous Wavelet Transform (CWT) is employed for feature extraction, and multiclass classification is performed using Error-Correcting Output Codes (ECOC). Under commercial load interference, conventional machine-learning classifiers achieve a macro classification accuracy of 89.53%, with the lowest class accuracy dropping to 76.74%. To address this limitation, a deep learning (DL)-based framework is implemented. Simulation results demonstrate that the proposed DL approach improves overall classification accuracy from 89.53% to 100%, corresponding to a 10.47 percentage-point absolute improvement, an 11.7% relative gain, and complete elimination of misclassification errors. Notably, the most affected charging station class (FCS₂) accuracy increases from 76.74% to 100%. These results demonstrate that the proposed deep learning framework reliably detects FCS activations even under overlapping, variable, and high-power commercial load conditions, enabling more efficient energy management and optimal utilization of electrical resources, reduced energy waste, and enhanced sustainability of EV charging infrastructure within commercial facilities. 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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33 pages, 4465 KB

Open AccessArticle

Environmentally Sustainable HVAC Management in Smart Buildings Using a Reinforcement Learning Framework SACEM

by Abdullah Alshammari, Ammar Ahmed E. Elhadi and Ashraf Osman Ibrahim

Sustainability 2026, 18(2), 1036; https://doi.org/10.3390/su18021036 - 20 Jan 2026

Cited by 1 | Viewed by 812

Abstract

Heating, ventilation, and air-conditioning (HVAC) systems dominate energy consumption in hot-climate buildings, where maintaining occupant comfort under extreme outdoor conditions remains a critical challenge, particularly under emerging time-of-use (TOU) electricity pricing schemes. While deep reinforcement learning (DRL) has shown promise for adaptive HVAC control, existing approaches often suffer from comfort violations, myopic decision making, and limited robustness to uncertainty. This paper proposes a comfort-first hybrid control framework that integrates Soft Actor–Critic (SAC) with a Cross-Entropy Method (CEM) refinement layer, referred to as SACEM. The framework combines data-efficient off-policy learning with short-horizon predictive optimization and safety-aware action projection to explicitly prioritize thermal comfort while minimizing energy use, operating cost, and peak demand. The control problem is formulated as a Markov Decision Process using a simplified thermal model representative of commercial buildings in hot desert climates. The proposed approach is evaluated through extensive simulation using Saudi Arabian summer weather conditions, realistic occupancy patterns, and a three-tier TOU electricity tariff. Performance is assessed against state-of-the-art baselines, including PPO, TD3, and standard SAC, using comfort, energy, cost, and peak demand metrics, complemented by ablation and disturbance-based stress tests. Results show that SACEM achieves a comfort score of 95.8%, while reducing energy consumption and operating cost by approximately 21% relative to the strongest baseline. The findings demonstrate that integrating comfort-dominant reward design with decision-time look-ahead yields robust, economically viable HVAC control suitable for deployment in hot-climate smart buildings. 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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31 pages, 3386 KB

Open AccessArticle

Upgrading Sustainability in Clean Energy: Optimization for Proton Exchange Membrane Fuel Cells Using Heterogeneous Comprehensive Learning Bald Eagle Search Algorithm

by Ahmed K. Ali, Ali Nasser Hussain, Mudhar A. Al-Obaidi and Sarmad Al-Anssari

Sustainability 2025, 17(21), 9729; https://doi.org/10.3390/su17219729 - 31 Oct 2025

Viewed by 755

Abstract

Clean energy applications widely recognize Proton Exchange Membrane Fuel Cells (PEMFCs) for their high efficiency and environmental compatibility. Accurate parameter identification of PEMFC models is essential for enhancing system performance and reliability, particularly under dynamic operating conditions. This paper presents a novel optimization-based approach called Heterogeneous Comprehensive Learning-Bald Eagle Search (HCLBES) with enhanced exploration and exploitation capabilities for the effective modeling of PEMFC. The algorithm combines the exploration strength of the Bald Eagle Search with comprehensive learning and heterogeneity mechanisms to achieve a balanced global and local search space. In this algorithm, the number of agents is divided into two subagents. Each subagent is assigned to focus solely on either exploration or exploitation. The comprehensive learning strategy generates exemplars for both subgroups. In the exploration sub-agent, exemplars are generated using the personal best experiences of agents within that same exploration space. The exploitation subagent generates the exemplars using the personal best experiences of all agents. This separation preserves exploration diversity even if exploitation converges prematurely. The algorithm is applied to optimize parameters of the 250 W and 500 W PEMFC models under varying conditions. Simulation results demonstrate the outperformance of the HCLBES algorithm in terms of convergence speed, estimation accuracy, and robustness compared to recent optimization algorithms. The effectiveness of HCLBES was also verified through statistical metrics and different commercial PEMFC models, including BCS 500 W stacks, Horizon 500, and NedStack PS6. Experimental validation confirms that the proposed algorithm effectively captures the nonlinear behaviours of PEMFCs under dynamic operating conditions. This research aligns with the Sustainable Development Goals (SDGs) by promoting clean and affordable energy (SDG 7) through the enhanced efficiency and reliability of PEMFCs, thereby supporting sustainable industrialization and innovation (SDG 9). 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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18 pages, 6195 KB

Open AccessArticle

Hybrid Wind Power Forecasting for Turbine Clusters: Integrating Spatiotemporal WGANs with Extreme Missing-Data Resilience

by Hongsheng Su, Yuwei Du, Yulong Che, Dan Li and Wenyao Su

Sustainability 2025, 17(20), 9200; https://doi.org/10.3390/su17209200 - 17 Oct 2025

Cited by 1 | Viewed by 1070

Abstract

The global pursuit of sustainable development amplifies renewable energy’s strategic importance, positioning wind power as a vital modern grid component. Accurate wind forecasting is essential to counter inherent volatility, enabling robust grid operations, security protocols, and optimization strategies. Such predictive precision directly governs wind energy systems’ stability and sustainability. This research introduces a novel spatio-temporal hybrid model integrating convolutional neural networks (CNN), bidirectional long short-term memory (BiLSTM), and graph convolutional networks (GCN) to extract temporal patterns and meteorological dynamics (wind speed, direction, temperature) across 134 wind turbines. Building upon conventional methods, our architecture captures turbine spatio-temporal correlations while assimilating multivariate meteorological characteristics. Addressing data integrity compromises from equipment failures and extreme weather-which undermine data-driven models-we implement Wasserstein GAN (WGAN) for generative missing-value interpolation. Validation across severe data loss scenarios (30–90% missing values) demonstrates the model’s enhanced predictive capacity. Rigorous benchmarking confirms significant accuracy improvements and reduced forecasting errors. 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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20 pages, 4173 KB

Open AccessArticle

Sustainability and Grid Reliability of Renewable Energy Expansion Projects in Saudi Arabia by 2030

by Abdulaziz Almutairi and Yousef Alhamed

Sustainability 2025, 17(10), 4493; https://doi.org/10.3390/su17104493 - 15 May 2025

Cited by 9 | Viewed by 5527

Abstract

The penetration of renewable energy, especially solar and wind, is increasing globally to promote a sustainable environment. However, in the Middle East, this momentum is slower compared to other regions, primarily due to abundant local fossil fuel reserves and historically low energy prices. This trend is shifting, with several countries, including the Kingdom of Saudi Arabia (KSA), setting ambitious goals. Specifically, KSA’s Vision 2030 aims to generate 50% of its energy from renewable sources by 2030. Due to favorable conditions for solar and wind, various mega-projects have either been completed or are underway in KSA. This study analyzes the potential and reliability impact of these projects on the power system through a three-step process. In the first step, all major projects are identified, and data related to these projects, such as global horizontal irradiance, wind speed, temperature, and other relevant parameters, are collected. In the second step, these data are used to estimate the solar and wind potential at various sites, along with annual averages and seasonal averages for different extreme seasons, such as winter and summer. Finally, in the third step, a reliability assessment of power generation is conducted to evaluate the adequacy of renewable projects within the national power grid. This study addresses a gap in the literature by providing a region-specific reliability analysis using actual project data from KSA, which remains underexplored in existing research. Sequential Monte Carlo simulations are employed, and various reliability indices, including Loss of Load Expectation (LOLE), Loss of Energy Expectation (LOEE), Loss of Load Frequency (LOLF), Energy Not Supplied per Interruption (ENSINT), and Demand Not Supplied per Interruption (DNSINT) are analyzed. The analysis shows that integrating renewable energy into KSA’s power grid significantly enhances its reliability. The analysis shows that integrating renewable energy into KSA’s power grid significantly enhances its reliability, with improvements observed across all reliability indices, demonstrating the viability of meeting Vision 2030 targets. 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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20 pages, 5663 KB

Open AccessArticle

Short-Term Output Scenario Generation of Renewable Energy Using Transformer–Wasserstein Generative Adversarial Nets-Gradient Penalty

by Liuqing Gu, Jian Xu, Deping Ke, Youhan Deng, Xiaojun Hua and Yi Yu

Sustainability 2024, 16(24), 10936; https://doi.org/10.3390/su162410936 - 13 Dec 2024

Cited by 10 | Viewed by 3577

Abstract

As renewable energy sources are becoming more widely integrated into the modern power system, the uncertainties within this system are becoming increasingly prominent. It is crucial to accurately describe the uncertainties in renewable energy output for the effective planning, scheduling, and control of power systems. For this purpose, the aim of this paper is to introduce a method for generating short-term output scenarios for renewable energy sources based on an improved Wasserstein Generative Adversarial Nets-Gradient Penalty. First, a Deep Neural Network structure inspired by the Transformer algorithm is developed to capture the temporal characteristics of renewable energy outputs. Then, combined with the advantage of the data generation of the Wasserstein Generative Adversarial Nets-Gradient Penalty, the Transformer–Wasserstein Generative Adversarial Nets-Gradient Penalty is proposed to generate short-term renewable energy output scenarios. Finally, experimental validation is conducted on open-source wind and photovoltaic datasets from the U.S. National Renewable Energy Laboratory, where the performance of the proposed model in generating renewable energy output scenarios across various aspects (i.e., individual sample representation, expectation and variance, probability density function, cumulative distribution function, power spectral density, autocorrelation coefficient, and pinball loss) is assessed. The results show that our method outperforms the Wasserstein Generative Adversarial Nets-Gradient Penalty, Variational Autoencoder, Copula function, and Latin Hypercube Sampling models in the abovementioned evaluation indicators, providing a more precise probability distribution representation of realistic short-term renewable energy outputs. 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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21 pages, 7765 KB

Open AccessArticle

Bayesian-Neural-Network-Based Approach for Probabilistic Prediction of Building-Energy Demands

by Akash Mahajan, Srijita Das, Wencong Su and Van-Hai Bui

Sustainability 2024, 16(22), 9943; https://doi.org/10.3390/su16229943 - 14 Nov 2024

Cited by 8 | Viewed by 3870

Abstract

Reliable prediction of building-level energy demand is crucial for the building managers to optimize and regulate energy consumption. Conventional prediction models omit the uncertainties associated with demand over time; hence, they are mostly inaccurate and unreliable. In this study, a Bayesian neural network (BNN)-based probabilistic prediction model is proposed to tackle this challenge. By quantifying the uncertainty, BNNs provide probabilistic predictions that capture the variations in the energy demand. The proposed model is trained and evaluated on a subset of the building operations dataset of Lawrence Berkeley National Laboratory (LBNL), Berkeley, California, which includes diverse attributes related to climate and key building-performance indicators. We have performed thorough hyperparameter tuning and used fixed-horizon validation to evaluate trained models on various test data to assess generalization ability. To validate the results, quantile random forest (QRF) was used as a benchmark. This study compared BNN with LSTM, showing that BNN outperformed LSTM in uncertainty quantification. 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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15 pages, 6431 KB

Open AccessArticle

Ensuring Sustainable Grid Stability through Effective EV Charging Management: A Time and Energy-Based Approach

by Saeed Alyami

Sustainability 2024, 16(14), 6149; https://doi.org/10.3390/su16146149 - 18 Jul 2024

Cited by 12 | Viewed by 4196

Abstract

The rise of electric vehicles (EVs) has significantly transformed transportation, offering environmental advantages by curbing greenhouse gas emissions and fossil fuel dependency. However, their increasing adoption poses challenges for power systems, especially distribution systems, due to the direct connection of EVs with them. It requires robust infrastructure development, smart grid integration, and effective charging solutions to mitigate issues like overloading and peak demand to ensure grid stability, reliability, and sustainability. To prevent local equipment overloading during peak load intervals, the management of EV charging demand is carried out in this study, considering both the time to deadline and the energy demand of EVs. Initially, EVs are prioritized based on these two factors (time and energy)—those with shorter deadlines and lower energy demands receive higher rankings. This prioritization aims to maximize the number of EVs with their energy demands met. Subsequently, energy allocation to EVs is determined by their rankings while adhering to the transformer’s capacity limits. The process begins with the highest-ranked EV and continues until the transformer nears its limit. To this end, an index is proposed to evaluate the performance of the proposed method in terms of unserved EVs during various peak load intervals. Comparative analysis against the earliest deadline first approach demonstrates the superior ability of the proposed method to fulfill the energy demand of a larger number of EVs. By ensuring sustainable energy management, the proposed method supports the widespread adoption of EVs and the transition to a cleaner, more sustainable transportation system. Comparative analysis shows that the proposed method fulfills the energy needs of up to 33% more EVs compared to the earliest deadline method, highlighting its superior performance in managing network loads. 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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44 pages, 2070 KB

Open AccessSystematic Review

A Systematic Review of Advances in Deep Learning Architectures for Efficient and Sustainable Photovoltaic Solar Tracking: Research Challenges and Future Directions

by Ali Alhazmi, Kholoud Maswadi and Christopher Ifeanyi Eke

Sustainability 2025, 17(21), 9625; https://doi.org/10.3390/su17219625 - 29 Oct 2025

Cited by 2 | Viewed by 1750

Abstract

The swift advancement of renewable energy technology has highlighted the need for effective photovoltaic (PV) solar energy tracking systems. Deep learning (DL) has surfaced as a promising method to improve the precision and efficacy of photovoltaic (PV) solar tracking by utilising complicated patterns in meteorological and PV system data. This systematic literature review (SLR) seeks to offer a thorough examination of the progress in deep learning architectures for photovoltaic solar energy tracking over the last decade (2016–2025). The review was structured around four research questions (RQs) aimed at identifying prevalent deep learning architectures, datasets, performance metrics, and issues within the context of deep learning-based PV solar tracking systems. The present research utilised SLR methodology to analyse 64 high-quality publications from reputed academic databases like IEEE Xplore, Science Direct, Springer, and MDPI. The results indicated that deep learning architectures, including Convolutional Neural Networks (CNNs), Long Short-Term Memory (LSTM) networks, and Transformer-based models, are extensively employed to improve the accuracy and efficiency of photovoltaic solar tracking systems. Widely utilised datasets comprised meteorological data, photovoltaic system data, time series data, temperature data, and image data. Performance metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Mean Absolute Percentage Error (MAPE), were employed to assess model efficacy. Identified significant challenges encompass inadequate data quality, restricted availability, high computing complexity, and issues in model generalisation. Future research should concentrate on enhancing data quality and accessibility, creating generalised models, minimising computational complexity, and integrating deep learning with real-time photovoltaic systems. Resolving these challenges would facilitate advancements in efficient, reliable, and sustainable photovoltaic solar tracking systems, hence promoting the wider adoption of renewable energy technology. This review emphasises the capability of deep learning to transform photovoltaic solar tracking and stresses the necessity for interdisciplinary collaboration to address current limitations. 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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