AI‐Driven Innovations in Solar Energy Systems and Climate Change Mitigation
太陽エネルギーシステムと気候変動緩和におけるAI主導の革新 (AI 翻訳)
Gandla Shivakanth, Ramakrishna Akella, V. Biksham, Alampally Sreedevi, Shiva Kumar Agraharam
🤖 gxceed AI 要約
日本語
本論文は、太陽エネルギーシステムと気候変動緩和における人工知能（AI）の役割の高まりを概説する。LSTM、CNN、ランダムフォレスト、コンピュータビジョンなどのAIモデルが、太陽光発電の予測、追跡、保守、ダスト検出、蓄電池管理、スマートグリッド最適化を効率化する。インドの国家太陽光ミッションやNTPC太陽光発電所などの事例を通じ、AIがエネルギー出力、信頼性、持続可能性を向上させることを示す。
English
This paper reviews the growing role of AI in solar energy systems and climate change mitigation. AI models such as LSTM, CNN, Random Forest, and computer vision enable efficient solar power forecasting, panel tracking, predictive maintenance, dust detection, battery management, and grid optimization. Real-world cases including India's National Solar Mission and NTPC solar farms demonstrate that AI improves energy output, reliability, and sustainability.
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効率化や維持管理にAI活用が進んでおり、本論文の知見は日本企業の運用改善に応用可能。特に、ダスト検出や予測保守は日本の気候条件にも有用。
In the global GX context
This survey provides a global overview of AI applications in solar energy, relevant for countries scaling up renewables. The India case studies offer insights for emerging economies, while the technical frameworks can inform global best practices in smart solar farm management.
👥 読者別の含意
🔬研究者:Provides a comprehensive taxonomy of AI techniques applied to solar energy and climate modeling, useful for identifying research gaps.
🏢実務担当者:Offers practical examples of AI implementation for solar farm optimization, predictive maintenance, and grid integration that can be adapted by corporate sustainability teams.
🏛政策担当者:Highlights policy-driven successes like India's National Solar Mission, demonstrating how AI can accelerate renewable energy targets.
📄 Abstract（原文）
This research paper highlights the growing role of Artificial Intelligence (AI) in solar energy systems and climate change mitigation. Solar energy has become the fastest-growing renewable source today, but it faces significant operational challenges such as intermittency, dust accumulation, equipment degradation, storage optimization, and grid integration. AI-based models—such as LSTM, CNN, Random Forest, and computer-vision techniques—enable highly efficient solar power forecasting, smart panel tracking, predictive maintenance, dust detection, battery management, and smart grid optimization. Similarly, in climate change management, AI provides accurate and real-time insights into rainfall prediction, cyclone tracking, pollution monitoring, carbon emission analysis, and disaster forecasting. Real-world cases like India's National Solar Mission, NTPC solar farms, Rajasthan desert solar parks, and Google's climate prediction systems demonstrate that AI is significantly improving energy output, reliability, and sustainability. Overall, AI makes solar energy smarter, reliable, and cost-efficient, and provides a data-driven and predictive framework for climate change mitigation. This integration is a strong step toward fully autonomous solar plants and intelligent climate governance in the future.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1002/9781394419494.ch14first seen 2026-05-05 19:13:53
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