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：研究統合 (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応用の国際研究を包括的に統合したものである。太陽光予測、故障検知、最大電力点追跡、スマートグリッド統合などの主要応用を分析し、ディープラーニングや強化学習などの高度手法の有効性を評価する。また、インドの気候条件やエッジAI展開に関する研究ギャップを特定する。
English
This paper presents a comprehensive synthesis of international research on AI applications in solar energy, covering forecasting, fault detection, MPPT, and smart grid integration. It analyzes advanced techniques like deep learning and reinforcement learning, highlighting their effectiveness. It identifies research gaps, particularly for Indian climatic conditions and edge AI deployment.
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導入が進んでおり、AIによる発電予測や保守最適化は重要である。本稿は日本の太陽光発電事業者や研究機関にとって、AI活用の最新動向を把握する有用な資料となる。
In the global GX context
Globally, AI is increasingly used to optimize renewable energy systems. This synthesis provides a comprehensive overview of AI applications in solar energy, offering insights for researchers and practitioners aiming to enhance efficiency and reliability of solar power.
👥 読者別の含意
🔬研究者:Provides a structured overview of AI techniques in solar energy, identifying key research gaps.
🏢実務担当者:Useful for solar farm operators considering AI for predictive maintenance and performance optimization.
🏛政策担当者:Highlights the potential of AI to support renewable energy targets, encouraging supportive policies for AI integration.
📄 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://doi.org/10.5281/zenodo.20038623first seen 2026-05-24 04:36:07 · last seen 2026-05-27 04:31:43
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