Artificial Intelligence-Based Decision Support Systems for Renewable Energy Investment and Deployment for Transition to Sustainable Energy
持続可能なエネルギーへの移行のための再生可能エネルギー投資と展開を支援する人工知能ベースの意思決定支援システム (AI 翻訳)
Venkata Krishna Reddy, Aadam Quraishi, G. C. Prashant, Mukesh Soni
🤖 gxceed AI 要約
日本語
本論文は、再生可能エネルギー（RE）投資と展開のための深層学習ベースの意思決定支援システム（DSS）を提案する。CNNで気象・エネルギー・経済データから特徴を抽出し、RNNで将来のエネルギー需要と供給を予測、強化学習でコスト削減とグリッド運用を最適化する。REの不安定性に対処し、持続可能なエネルギー移行を支援する。
English
This paper proposes a deep learning-based decision support system (DSS) for renewable energy (RE) investment and deployment. It uses CNNs to extract features from weather, energy, and economic data, RNNs to forecast energy demand and supply, and reinforcement learning to optimize costs and grid operations. The DSS addresses RE intermittency and supports the transition to sustainable energy.
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
日本では、再生可能エネルギーの導入拡大と系統安定化が課題であり、AIを活用したDSSはFIT/FIP制度下での投資判断や電力市場運用に貢献できる。SSBJ開示における再エネ調達戦略の策定にも有用。
In the global GX context
Globally, this DSS aligns with the need for advanced tools to integrate variable renewables into grids, supporting energy transition goals. It offers a data-driven approach for investment decisions and grid management, relevant to TCFD/ISSB climate transition planning and sustainable finance.
👥 読者別の含意
🔬研究者:AIと再エネ統合の交差点におけるDSS設計の参考になる。
🏢実務担当者:再エネ投資判断や系統運用の最適化に活用できる。
🏛政策担当者:再エネ導入促進政策の効果評価に示唆を与える。
📄 Abstract（原文）
This chapter describes an innovative deep learning-based decision support system (DSS) for renewable energy (RE) investment and deployment. The proposed DSS employs convolutional neural networks ( https://www.w3.org/1998/Math/MathML" display="inline"> CNN s) to extract characteristics from historical weather, energy, and economic statistics data. Following that, the characteristics are input into a recurrent neural network ( https://www.w3.org/1998/Math/MathML" display="inline"> RNN ) that has been trained to estimate future energy use and output. Finally, to reduce total energy expenditures and enhance grid operations, a technology known as reinforcement learning is used. To enable the transition to sustainable energy, the deployment of RE sources 120 must be considerably enhanced. However, because RE is unreliable and intermittent, incorporating it into the grid creates several issues. DSS based on artificial intelligence can assist in resolving these issues by providing information on RE production, demand, and grid operations. This research suggests a ground-breaking deep learning-based DSS for funding and implementing RE projects. The proposed DSS employs https://www.w3.org/1998/Math/MathML" display="inline"> CNN s to extract characteristics from historical weather, energy, and economic statistics data. Following that, the characteristics are input into a https://www.w3.org/1998/Math/MathML" display="inline"> RNN that has been trained to estimate future energy use and output.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1201/9781779640451-8first seen 2026-08-07 04:55:07
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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。