Solar Forecasting Methods: Transition from Classical to Artificial Intelligence-based Forecasting
太陽光発電予測手法：古典的手法から人工知能ベースの予測への移行 (AI 翻訳)
Ahmet Önen
🤖 gxceed AI 要約
日本語
本稿は、太陽光発電予測手法に関する包括的なサーベイであり、古典的手法から人工知能（AI）やハイブリッド手法への進化を整理している。予測精度向上に向けた最新の手法や戦略を比較・分析し、AI技術の活用動向を捉えている。系統安定化や大規模PV導入促進に資する予測技術の現状を明らかにする。
English
This paper provides a comprehensive survey of solar energy forecasting methods, tracking the transition from classical approaches to AI-based and hybrid techniques. It reviews recent strategies to improve prediction accuracy, including classification models and trend analysis. The study highlights the role of AI in enhancing grid stability and supporting large-scale PV integration.
Unofficial AI-generated summary based on the public title and abstract. Not an official translation.
📝 gxceed 編集解説 — Why this matters
日本のGX文脈において
日本は太陽光発電の導入が進んでおり、出力変動対策として高精度な予測技術の重要性が増している。本サーベイは、AI技術を含む最新手法の動向を整理しており、日本の電力系統運用者や再生可能エネルギー事業者にとって参考になる。
In the global GX context
Solar forecasting is critical for grid stability and high PV penetration worldwide. This survey summarizes advanced AI and hybrid methods, offering a valuable reference for utilities and system operators integrating variable renewable energy.
👥 読者別の含意
🔬研究者:Provides a structured overview of forecasting methodologies, from classical to AI, useful for identifying research gaps and emerging trends.
🏢実務担当者:Offers a comparative analysis of forecasting techniques that can guide selection and implementation for solar power plant operators and grid managers.
🏛政策担当者:Highlights the importance of forecasting for renewable integration, supporting policy decisions on grid modernization and renewable energy targets.
📄 Abstract（原文）
The variability in photovoltaic power generation generates different negative effects on power grid systems in terms of stability, reliability, and operation planning. Therefore, an accurate estimation of PV power generation is crucial for stabilizing and securing grid operation and promoting large-scale PV power integration. Every year, new techniques and approaches emerge worldwide that reduce the uncertainty in these estimates and improve model accuracy. This study presents a comprehensive survey of solar energy prediction models while summarizing the most recent methodologies and strategies employed to enhance the precision of solar energy production forecasting. The energy sector is highly dynamic and integrates new technologies continually; hence, compilation studies should be updated with new developments. Industries are trying to benefit from artificial intelligence, including the field of solar energy prediction, and this study attempts to capture this trend. In addition to presenting a comparison of the solution methods in artificial intelligence, hybrid approaches to overcome problems are discussed. Different classification models and critical analyses of recent studies based on forecast horizons and historical data are also presented.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.31202/ecjse.1835162first seen 2026-05-27 04:35:03
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