風力エネルギー生産予測のための人工知能モデルの評価：Methodi Ordinatio 2.0に基づく系統的文献レビュー

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:Provides a comprehensive map of AI techniques for wind forecasting, highlighting research gaps and emerging trends like foundation models and XAI.

🏢実務担当者:Offers guidance on selecting AI models for wind power prediction, aiding in operational efficiency and renewable energy integration.

🏛政策担当者:Informs policies supporting AI adoption in renewable energy to enhance grid stability and meet decarbonization targets.

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.

• openalex https://doi.org/10.1007/s40866-026-00363-8first seen 2026-08-21 04:32:29