Fundamentals of Green Energy and AI
グリーンエネルギーとAIの基礎 (AI 翻訳)
Saumen Dhara, Shantanu Naskar, Rudrajit Datta
🤖 gxceed AI 要約
日本語
再生可能エネルギー（太陽光、風力、水力、バイオマス、地熱）の基礎と、AI（機械学習、深層学習、強化学習）による予測、スマートグリッド最適化、カーボンモニタリングへの応用を概説する章。エネルギー転換におけるAIの役割を包括的に解説。
English
This chapter provides a foundational overview of green energy systems (solar, wind, hydro, biomass, geothermal) and the integration of AI techniques (ML, DL, RL) for forecasting, smart grid optimization, predictive maintenance, and carbon-aware operations. It discusses challenges of intermittency and storage, positioning AI as key to resilient, zero-carbon energy systems.
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
日本のGX政策（第6次エネルギー基本計画、グリーン成長戦略）では、再生可能エネルギーとAI・デジタル技術の融合が重点分野。本稿は系統安定化や需要予測などの基礎を提供し、実務者・研究者双方に有用。
In the global GX context
Globally, AI-driven energy optimization is a critical pillar of the energy transition, supporting grid stability, renewables integration, and carbon reduction. This chapter offers a concise entry point for understanding the technical landscape, relevant to ISSB/TCFD-related disclosures on energy transition risks.
👥 読者別の含意
🔬研究者:Useful as an introductory survey for newcomers to AI applications in renewable energy systems.
🏢実務担当者:Provides a framework for considering AI adoption in energy management and grid operations.
🏛政策担当者:Highlights the importance of R&D in AI for smart grid and renewable integration to support decarbonization targets.
📄 Abstract（原文）
The integration of Green Energy Systems (GES) and Artificial Intelligence (AI) has gained increased pace as part of the global shift to sustainable and intelligent energy infrastructures, in order to combat climate change.The global transition from non-sustainable and non-intelligent energy infrastructures to sustainable and intelligent energy infrastructures has accelerated the use of Artificial Intelligence (AI) together with Green Energy Systems (GES) to face climate change.The present chapter proposes the fundamental principles, developments and applications of renewable energy technologies like solar, wind, hydro power, biomass, and geo thermal sources in smart power grid in modern scenario.It underscores the increasing environmental need for clean energy solutions, with the challenges of climate change, carbon emission reduction, energy security and sustainable economic development.In more depth, the chapter discusses the operational problems of intermittency and grid instability of renewable sources, energy models for energy forecasting and the limitations of energy storage.To address these hurdles, the solutions implemented involve advanced AI techniques such as Machine Learning (ML), Deep Learning (DL), and Reinforcement Learning (RL) for renewable energy forecasting, predictive maintenance, smart grid optimization, autonomous energy management and carbon aware operations.The chapter highlights the need for intelligent energy management systems, smart grids, sustainable computing, and AI-based carbon monitoring systems to create resilient, efficient, and environmentally friendly future energy systems.This chapter provides a good illustration of the combination of renewable energy technologies and intelligent computational intelligence, thus setting the groundwork for cleaner, smarter, decentralized and sustainable power systems that can meet future global needs for energy and deliver the objectives of a zero-emission path to a zero-carbon world.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.70965/geaiimasps-eb.2026.1first seen 2026-07-13 05:01:00
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gxceed は公開メタデータに基づく研究支援データセットです。要約・翻訳・解説は AI 支援で生成されています。 最終的な解釈・検証は利用者が原典資料に基づいて行うことを前提とします。