Aug 2026· Global Journal of Engineering and Technology Advances· 0 citations
TL;DR
Emerging research directions, such as explainable AI, federated learning, digital twins, edge intelligence, and physics-informed machine learning, are identified as promising strategies for developing resilient, intelligent, and sustainable future power grids.
Abstract
The global transition to renewable energy is critical for climate change mitigation, energy security, and the achievement of sustainable development goals. Nevertheless, the intermittent and unpredictable characteristics of renewable energy sources, particularly solar and wind, pose substantial challenges for reliable power grid operation and energy management. Artificial Intelligence (AI) has emerged as a transformative technology that enhances forecasting accuracy and enables intelligent decision-making for renewable energy integration. This paper reviews recent advances in AI-driven forecasting techniques that facilitate the efficient integration of renewable energy systems into power grids. Relevant studies were identified through a systematic search of peer-reviewed literature published in major scientific databases over the past decade, with an emphasis on works that applied AI methods to renewable energy forecasting and energy management. Studies were selected and synthesized based on criteria including methodological rigor, relevance to grid integration, and demonstrated impact on operational performance. The review examines the application of machine learning, deep learning, reinforcement learning, and hybrid AI models in forecasting renewable energy generation, electricity demand, energy storage management, and grid optimization. The review also discusses data acquisition and preprocessing methods, forecasting architectures, performance evaluation metrics, and real-world case studies that demonstrate the effectiveness of AI-enabled energy management systems. The findings indicate that AI-based forecasting substantially improves prediction accuracy, enhances grid stability, optimizes energy dispatch, reduces operational costs, and supports greater penetration of renewable energy resources. Despite these advancements, challenges related to data quality, computational complexity, cybersecurity, model interpretability, and regulatory frameworks continue to impede large-scale deployment. The paper concludes by identifying emerging research directions, such as explainable AI, federated learning, digital twins, edge intelligence, and physics-informed machine learning, as promising strategies for developing resilient, intelligent, and sustainable future power grids.
The extensive deployment of renewable energy sources (RES) across modern energy infrastructure has introduced significant operational complexity, necessitating the development of advanced data-driven control strategies to ensure reliable and efficient system operation. Among these approaches, reinforcement learning (RL) has emerged as a promising paradigm for managing renewable generation and coordinating interconnected energy subsystems under uncertainty and dynamic operating conditions. The current paper presents a comprehensive review of RL-based control applications across RES-integrated energy domains, including power grids, microgrids, and building energy systems. The paper begins by outlining the fundamental characteristics of these smart grid energy environments along with the mathematical foundations of RL and its principal algorithmic families. A structured analysis of recent peer-reviewed studies is then conducted, with the literature systematically categorized according to the corresponding energy domain. A high number of impactful selected studies are further examined across multiple key dimensions, including RL methodologies, agent architectures, reward design, baseline control strategies, RES-integrated technologies, and control objectives. Based on this multi-dimensional evaluation, the review identifies emerging trends and highlights dominant design patterns across power grid, microgrid, and building-level applications. Finally, the observations are critically discussed and future research directions are outlined towards the development of scalable, practical, and reliable RL-based energy management solutions for next-generation smart grid systems.
P. Michailidis, F. Minelli, H. Coban et al.· Infrastructures· 0 citations
This narrative review examines the state of the art in modelling solar energy production in energy communities, with a particular focus on photovoltaic systems. It explores a wide range of approaches, from classical parametric models to intelligent techniques such as machine learning and deep learning. It identifies key methods, their applications and limitations, with an emphasis on the transition from static models linked to physical system parameters to dynamic and data-driven approaches using weather data and historical data inputs. It further identifies a specific gap in the current literature: the predominance of short-term forecasting over real-time estimation and the limited integration of intelligent techniques with dynamic sharing coefficients and peer-to-peer exchange schemes. Synthesising advances in peer-to-peer energy exchange models, distributed generation frameworks, and predictive optimisation systems, this review highlights the integration of intelligent techniques as a promising direction for improving the management of renewable energy communities, since such techniques are data-driven and decoupled from the physical structure of the photovoltaic system.
Anabel Díaz-Labrador, J. M. González-Cava, Héctor Quintián et al.· Energies· 0 citations
Growing reliance on distributed, weather-dependent renewable sources brings sizable variability, uncertainty, and stability problems to today's power grids. This work proposes an AI-driven digital twin (DT) framework that pairs a real-time physical-data grid model with a layered AI optimisation engine made up of an LSTM/Transformer forecaster, a graph neural network (GNN) state estimator, and a deep reinforcement learning (DRL) dispatch controller. The twin continually mirrors the physical grid, supporting scenario simulation, predictive fault detection, and closed-loop control recommendations before any action is applied to the real system. A case study on a modified IEEE 33-bus feeder with solar PV and wind generation shows the framework raises renewable utilisation by 19 points, cuts curtailment by 28 points, and lowers fault response time by 68.2% versus a conventional energy-management system. These results suggest AI-enhanced digital twins are a scalable route to self-optimising, resilient renewable-heavy grids.
Dr. G. Sripriya, V. S. Guhan, A. S. Nandha Kisore· International Journal of Adv...· 0 citations
With rising global energy demand and growing awareness of climate change and its impacts, the share of renewable energies in the global energy mix continues to grow. Unlike conventional power generation, the output of renewable energy sources cannot be controlled as consistently due to their dependence on environmental conditions. Therefore, reliable prediction of current and future energy production is essential. In this paper, we report findings from two structured literature reviews on real-world renewable energy prediction tasks: wind turbine power curve modeling and photovoltaic power prediction. For the former, we conducted a comprehensive literature review ourselves, while for the latter, we synthesize the key findings regarding frequently selected input features based on an existing survey. Across both domains, our analysis reveals that despite the large number of available monitoring and environmental variables, only limited or unsystematic methods for feature selection exist. To address this gap, we propose Cluster-based Sequential Feature Selection (CSFS), a novel, model-agnostic, clustering-based wrapper method for automatic, efficient, and reliable feature selection in renewable energy prediction pipelines. To support reproducibility and reuse, we provide an open-source implementation of CSFS on GitHub. We empirically evaluate the proposed approach on both use cases and compare it with established feature selection techniques such as wrapper-based sequential feature selection (SFS), filter-based methods, and Random Forest's embedded feature importance. The results show that the wrapper-based methods overall provide better-performing selections of features. CSFS achieves a predictive performance comparable to SFS while reducing computational cost by an average of 21%.
Daniel Grillmeyer, Marius Hadry, Michael Stenger et al.· 0 citations
The accurate prediction of renewable energy generation is essential for the efficient operation, planning, and optimization of modern power systems. The increasing integration of renewable sources such as solar photovoltaic (PV) and wind energy introduces uncertainty because their output depends on meteorological conditions. Traditional statistical and physics-based forecasting methods often fail to effectively capture the complex, nonlinear, and stochastic characteristics of renewable energy time-series data. Deep learning models have recently emerged as powerful tools for renewable energy forecasting due to their ability to automatically learn hierarchical representations from large-scale historical and exogenous data. This study presents a comprehensive analysis of renewable energy forecasting using deep learning techniques, focusing on short-term and medium-term prediction horizons. The paper reviews the evolution of forecasting approaches from conventional time-series models to advanced deep learning architectures such as convolutional neural networks (CNNs), recurrent neural networks (RNNs), long short-term memory (LSTM) networks, gated recurrent units (GRUs), and hybrid deep learning models. The proposed forecasting framework integrates meteorological variables, historical power generation data, and temporal features to improve prediction accuracy and robustness. A unified deep learning–based forecasting scheme is presented, including data collection, preprocessing, feature engineering, model training, validation, and deployment. Mathematical formulations of the learning process and loss optimization are also provided to establish a theoretical foundation. Extensive experiments are conducted on real-world renewable energy datasets obtained from publicly available sources to demonstrate the superiority of deep learning models over traditional methods. Model performance is evaluated using Mean Absolute Error (MAE), Root Mean Square Error (RMSE), and Mean Absolute Percentage Error (MAPE). The results show that deep learning models provide significantly better forecasting accuracy, particularly under highly variable weather conditions. The study highlights the importance of model selection, hyperparameter optimization, and data quality in renewable energy forecasting and serves as a useful reference for researchers and practitioners working on intelligent energy management and decision-support systems.
Lucas Martin, Chloe Bernard· International Journal of Mod...· 0 citations
The intermittent and volatile characteristics of new energy generation, together with the increasing demand for stable power supply in intelligent industrial systems, make accurate forecasting a critical issue for grid dispatch and electromagnetic energy management. This study systematically reviews the technological evolution of time series analysis methods for wind and photovoltaic (PV) power forecasting and establishes a comparative framework covering classical statistical models, intelligent learning algorithms, and hybrid modeling strategies. Based on two years of operational data collected from an actual wind farm and PV station in East China, the forecasting performance of ARIMA, exponential smoothing, Support Vector Regression (SVR), Long Short-Term Memory (LSTM) networks, and Transformer architectures is comprehensively evaluated, while hybrid approaches based on Empirical Mode Decomposition (EMD) are further investigated. The results demonstrate that model selection should jointly consider forecasting horizon, data characteristics, and computational constraints. Classical statistical methods remain robust under stable operating conditions but are less effective in capturing extreme fluctuations, whereas deep learning approaches exhibit superior capability in modeling long-range temporal dependencies despite reduced interpretability. Decomposition-based hybrid strategies achieve a more balanced performance across diverse scenarios and show enhanced robustness under extreme weather conditions. The study further proposes a structured model selection guideline by matching data characteristics with operational requirements, providing theoretical support for forecasting system design in renewable-energy-driven power networks and offering useful references for electromagnetic energy utilization and intelligent industrial applications.
M. Song, C. Yang, Z. Heng et al.· Advanced Electromagnetics· 0 citations