The results indicate that while advancements in AI offer significant benefits for wind energy production, challenges persist regarding model complexity, data quality, and the need for stronger collaboration between academia and industry to enable large-scale implementation.
Abstract
This paper presents a systematic review of the literature on the application of Artificial Intelligence (AI) techniques in the wind energy sector, aiming to identify trends, challenges, and opportunities in this evolving field. The increasing demand for renewable energy sources, combined with advancements in AI technologies, has fueled research efforts focused on optimizing the performance and efficiency of wind energy systems. In this context, various AI approaches, including artificial neural networks, machine learning algorithms, and optimization techniques, have been explored to enhance energy production forecasting. To ensure a comprehensive analysis that accurately reflects the current state-of-the-art, this study investigates a consolidated timeframe from 2015 to 2026 using the Methodi Ordinatio 2.0 methodology. By ranking a rigorously filtered portfolio of 400 experimental articles based on annualized citation density and Journal Impact Factor, this approach prevents temporal bias against recent advancements. The review highlights a critical architectural transition in the field: the historical predominance of Recurrent Neural Networks (RNNs/LSTMs) and the current paradigm shift toward Attention-based mechanisms and Transformers, which demonstrate superior capabilities in handling long-term dependencies in volatile wind speed time series. The results indicate that while advancements in AI offer significant benefits for wind energy production, challenges persist regarding model complexity, data quality, and the need for stronger collaboration between academia and industry to enable large-scale implementation. Furthermore, this study identifies specific emerging trends, emphasizing the adaptation of Foundation Models for time-series forecasting, the critical need for Explainable AI (XAI) to ensure operational trust, and the trade-off between computational cost and accuracy in edge computing for wind farms.
The rapid global expansion of solar photovoltaic (PV) capacity has increased the operational need for accurate generation forecasting to support grid balancing, dispatch, and market participation. Artificial intelligence (AI) and machine learning (ML) methods now dominate this research area, but the resulting literature is large and methodologically fragmented, making it difficult to establish which methods are used, what data they require, and where the principal gaps lie. This paper combines a bibliometric analysis of 3111 records retrieved from the Web of Science Core Collection (2010–2026) with a technical synthesis of 27 highly cited studies published from 2022 onward, combining the most highly cited works with targeted additions from 2024–2025 covering specific methodological gaps. The bibliometric analysis shows exponential growth in annual output, from three publications in 2010 to 609 in 2025, with keyword evolution tracing a clear methodological trajectory from classical and fuzzy-logic approaches, through shallow and deep neural networks, to transformer- and attention-based architectures since 2023. The technical synthesis finds that classical machine learning remains competitive for day-ahead forecasting with well-structured numerical weather prediction inputs, that convolutional neural network–long short-term memory (CNN-LSTM) hybrids dominate the deep-learning literature, and that graph-based and transformer architectures address multi-site and multi-horizon forecasting, respectively. A comparison of reported results shows that absolute error metrics are not directly comparable across studies due to heterogeneous datasets, metrics, temporal resolutions, and climates, although relative improvements within controlled comparisons are directionally consistent. Seven research gaps are identified, including the absence of standardized benchmarks, limited public dataset availability, weak cross-region generalization, and underdeveloped uncertainty quantification.
František Kurimský, Kamil Ševc, Marek Pavlík· Solar· 0 citations
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.
Olatunde Ibiyinka, Tolu Omotoso, N. Ekekwe· Global Journal of Engineerin...· 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. S. Song, C. Yang, Z. Heng et al.· Advanced Electromagnetics· 0 citations
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, is proposed and empirically evaluated on both use cases.
Daniel Grillmeyer, Marius Hadry, Michael Stenger et al.· arXiv.org· 0 citations
Accurate wind power forecasting is essential for the reliable operation and large-scale integration of renewable energy into modern power grids. This study develops and systematically evaluates a hybrid computational intelligence framework that integrates advanced machine learning models with nature-inspired optimization algorithms for wind power prediction. CatBoost (CAT), Long Short-Term Memory (LSTM), and Adaptive Neuro-Fuzzy Inference System (ANFIS) models were optimized using Cuckoo Search Optimization (CSO) and the Stochastic Paint Optimizer (SPO) to determine the most effective model–optimizer configuration under variable wind conditions. A comparative analysis demonstrates that the CAT–SPO hybrid model achieved the best predictive performance, yielding a test RMSE of 0.0338 and an R² of 0.984, outperforming alternative configurations. Feature relevance analysis and multicollinearity assessment using the Variance Inflation Factor (VIF) identified hub-height wind speed (100 m) as the dominant predictor (32.5% relative importance; VIF ≈ 3.96), while lower-height wind speed (10 m) was excluded due to high collinearity. Wind gust measurements at 10 m retained substantial explanatory contribution (≈ 19.2% importance; VIF ≈ 4.34), highlighting the role of short-term atmospheric variability in power modeling. The proposed framework enhances forecasting reliability and supports improved grid stability, reserve allocation, renewable energy integration, and data-driven operational planning. These findings advance intelligent energy management systems and sustainable power grid engineering.
Tariq Alkhrissat, Fıras Abed, S. Aldulaimi et al.· Scientific Reports· 0 citations
Accurate forecast of electricity demand has become essential for modern energy systems, which are Face-off escalating challenges because of industrial expansion, population growth, and the incorporation of renewable energy sources. With the development of machine learning methods, one can now efficiently forecast power consumption with the help of past data. This paper introduces a machine learning-based predictor of power consumption. We analyze various machine learning techniques, i.e., random forest, XGBoost, linear regression for power forecasting, in this work. These models were trained and tested on historical electricity consumption data from the Ministry of Electricity of Iraq, 2022 to 2025. The models' performance was evaluated using a number of metrics, such as Mean Absolute Error, Root Mean Squared Error, Mean absolute percentage error, and R-squared. the best performance model for demand was XGBoost, the model achieved on R-squared value of 0.98 and Linear regression model achieved on R-squared value of 0.98 for supply.
Maryam Jamal Abdulhameed, Ali Hasan Taresh· Iraqi Journal for Computers...· 0 citations
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