AI Applications in the Solar Renewable Energy Sector: A Research Synthesis
太陽光再生可能エネルギー分野におけるAI応用：研究合成 (AI 翻訳)
Karan Barot, Prof. M. J. Patel
🤖 gxceed AI 要約
日本語
本稿は太陽光発電分野におけるAI活用の国際研究を包括的にレビューし、日射量予測、故障検知、MPPT、スマートグリッド統合などの応用を分析。深層学習や強化学習の有効性を評価し、特にインドの気候条件における研究ギャップを指摘。AIによるエネルギー収量向上と運用コスト削減の可能性を示す。
English
This paper provides a comprehensive synthesis of international research on AI applications in solar energy, covering forecasting, fault detection, MPPT, and smart grid integration. It analyzes deep learning, reinforcement learning, and federated learning for real-world effectiveness, identifies research gaps especially for Indian climatic conditions, and concludes that AI can significantly enhance energy yield and reduce operational costs.
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
日本でも太陽光発電の大量導入に伴う出力変動対策や保守効率化が課題となっており、本レビューが示すAI技術の適用例は、日本の再生可能エネルギー統合やメンテナンス最適化に示唆を与える。ただし、日本の気候・制度条件に特化した議論は含まれていない。
In the global GX context
As solar energy deployment accelerates globally, this synthesis offers a timely overview of AI techniques for improving solar system performance. While the paper highlights Indian conditions, its findings on forecasting, predictive maintenance, and grid integration are broadly applicable to global renewable energy systems, providing a useful reference for both researchers and practitioners.
👥 読者別の含意
🔬研究者:Provides a structured literature review of AI in solar energy, identifying key techniques and research gaps that can guide future studies.
🏢実務担当者:Offers an overview of AI applications for solar farm operators, such as forecasting and predictive maintenance, to improve efficiency and reduce costs.
🏛政策担当者:Shows how AI can support renewable energy targets by enhancing grid stability and system reliability, but does not provide specific policy recommendations.
📄 Abstract（原文）
The rapid growth of the solar renewable energy sector is driving the need for smarter and more efficient system operation. However, challenges such as intermittent energy generation, forecasting inaccuracies, and high operation and maintenance costs continue to limit its full potential. This study presents a comprehensive synthesis of international research on the application of Artificial Intelligence (AI) in the solar energy domain, highlighting its role in improving performance, reliability, and decision-making across the value chain. The paper examines key AI applications including solar irradiance and power forecasting, fault detection and predictive maintenance, maximum power point tracking (MPPT), solar tracking optimization, and smart grid integration with energy storage systems. Advanced techniques such as deep learning (CNN-LSTM, Transformers), reinforcement learning, and federated learning are analyzed for their effectiveness in real-world scenarios. Furthermore, the study identifies critical research gaps, particularly in the context of Indian climatic conditions, edge AI deployment, and model interpretability. The findings suggest that AI-driven approaches can significantly enhance energy yield, reduce operational costs, and enable the transition toward intelligent, data-driven solar power systems.
🔗 Provenance — このレコードを発見したソース
- openalex https://www.ijert.org/ai-applications-in-the-solar-renewable-energy-sector-a-research-synthesisfirst seen 2026-05-24 04:31:58 · last seen 2026-06-04 04:33:32
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