🤖 gxceed AI 要約
日本語
本稿はAIが企業のエネルギー管理と脱炭素化に果たす役割を概説。リアルタイムデータや予測技術により再生可能エネルギーの活用、コスト削減、排出削減が可能となると説明。事例を通じて競争優位に変える可能性を示す一方、データ品質やサイバーセキュリティなどの課題も指摘。
English
This paper reviews how AI enables businesses to manage energy more efficiently and reduce emissions. It discusses AI's role in forecasting renewable generation, automating distributed assets, and participating in energy markets. Real-world cases show energy becoming a strategic advantage. Challenges like data quality, cybersecurity, and ethics are also addressed.
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GX推進の一環としてAI活用が注目されており、本稿は企業のエネルギー管理システム高度化や省エネ法対応に示唆を与える。SSBJ開示においてもエネルギー効率改善の実践的知見として活用可能。
In the global GX context
This paper contributes to the global discussion on how AI can accelerate corporate decarbonization, relevant to TCFD/ISSB disclosures and energy transition strategies. It offers practical insights for integrating AI into energy management, though lacks specific empirical evidence.
👥 読者別の含意
🔬研究者:Provides a broad overview of AI applications in energy efficiency, useful for framing research questions.
🏢実務担当者:Corporate sustainability teams can gain ideas for leveraging AI to reduce energy costs and emissions.
🏛政策担当者:Regulators may note the potential and barriers of AI for energy policy and grid management.
📄 Abstract（原文）
Artificial Intelligence (AI) is reshaping the way businesses manage energy, optimize operations, and pursue sustainability goals. As global industries transition toward decarbonization, AI provides the analytical and decision-making capabilities required to navigate increasingly complex energy systems. By leveraging real-time data from distributed energy resources, weather forecasts, and consumption profiles, AI platforms can forecast renewable generation, predict energy consumption, and automate control of distributed assets such as solar generation and energy storage. These capabilities allow businesses to reduce energy costs, improve operational resilience, and lower greenhouse gas emissions. In doing so, energy becomes not merely an operational cost but a strategic advantage. Real-world examples illustrate how AI enables companies to participate in emerging energy markets and demand response programs, transforming energy from a passive expense into an active source of competitive advantage. The discussion also examines key challenges, including data quality, cybersecurity, and regulatory barriers, as well as ethical considerations surrounding transparency and fairness. Ultimately, integrating AI into corporate energy management systems enables organizations to pursue both profitability and sustainability simultaneously, advancing the broader transition to an intelligent, low-carbon economy.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.4324/9781003715689-11first seen 2026-05-25 04:42:56 · last seen 2026-06-05 04:49:31
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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。