An Artificial Intelligence–Enabled Assessment Framework for Energy Conservation in Retrofitted and Operational Buildings
人工知能を活用した改修および運用中の建物における省エネルギー評価フレームワーク (AI 翻訳)
Zhiyong Wu, Yun Qiao, Youren Ma
🤖 gxceed AI 要約
日本語
本論文は、ASEAN地域の建物の省エネルギー性能を評価するためのAI・機械学習を活用した標準的枠組みを提案する。Energy Use Intensity（EUI）や気象データなどを統合し、従来の静的モデルより高精度なエネルギー削減量の定量化を実現する。商業・公共・複合用途の建物に適用可能で、グリーンファイナンスやデジタルMRVシステムとの連携も視野に入れている。
English
This paper proposes a standardized, AI-driven framework for assessing energy conservation in retrofitted and operational buildings across ASEAN. It integrates EUI, operational patterns, equipment loads, and local climate data to create adaptive baseline models that quantify savings more accurately than static methods. The framework supports green financing, performance-based procurement, and digital MRV systems for regional decarbonization.
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
本フレームワークはASEAN向けだが、日本でのZEBや既存ビルの省エネ評価にも応用可能。特にAIを用いた動的ベースラインモデルは、日本の不動産事業者やESCO事業にとって有用な手法となる。日本のSSBJやグリーンビルディング認証制度との連携も期待される。
In the global GX context
While focused on ASEAN, the framework offers a replicable AI methodology for energy performance assessment that complements global standards like ISO 50001 and supports climate disclosures under ISSB and TCFD. It provides a practical tool for MRV and green financing in emerging economies.
👥 読者別の含意
🔬研究者:Offers a data-driven methodology combining AI and building energy modeling for performance assessment, applicable to retrofit effectiveness studies.
🏢実務担当者:Facility owners and energy managers can use this framework to verify savings, support green building certifications, and access sustainability-linked incentives.
🏛政策担当者:Provides a standardized assessment approach that aligns with APAEC and national building codes, enabling consistent MRV for regional energy efficiency targets.
📄 Abstract（原文）
Accelerating energy efficiency improvements in the built environment is central to ASEAN’s decarbonization pathway and long-term climate commitments. However, the diverse operational profiles, climatic conditions, and retrofit strategies across the region pose significant challenges for consistent and credible assessment of energy conservation performance. To address this gap, this paper presents a standardized, data-driven evaluation framework that harnesses artificial intelligence (AI) and machine learning (ML) to analyze and verify energy performance in both retrofitted and actively operating buildings. Designed to be compatible with regional policies—including the objectives of the ASEAN Plan of Action for Energy Cooperation (APAEC), national green building rating tools, and emerging carbon management regulations—the framework integrates core indicators such as Energy Use Intensity (EUI), temporal operation patterns, equipment load profiles, and localized meteorological datasets. These inputs are synthesized to establish adaptive baseline models capable of capturing real-time performance variations and quantifying energy savings with higher accuracy than conventional static or rule-based methods. The proposed methodology is structured for application across commercial, institutional, and mixed-use developments, enabling facility owners, policymakers, and financial institutions to systematically track conservation outcomes and evaluate the effectiveness of retrofit interventions. Beyond performance verification, the framework is positioned to support regional green financing ecosystems, including sustainability-linked incentives, performance-based procurement, and digital MRV (measurement, reporting, and verification) systems increasingly adopted across ASEAN.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1051/e3sconf/202671301005/pdffirst seen 2026-06-14 04:26:49 · last seen 2026-06-14 04:37:51
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