AI-Enabled Generative Design Digital Twin Framework for Net-Zero Building Optimization Across European Climate Zones
欧州気候帯におけるネットゼロ建築最適化のためのAI対応生成設計デジタルツインフレームワーク (AI 翻訳)
Suhib O. A. Amro, Sepanta Naimi, Changiz Ahbab
🤖 gxceed AI 要約
日本語
建設部門の脱炭素化に向け、AI生成設計と機械学習サロゲートモデルを統合した最適化フレームワークを提案。20変数のパラメトリック設計空間でNSGA-IIIとPSOを併用し、物理シミュレーションを500〜850倍高速化しつつ95%以上の精度を維持。欧州7カ国の12の改修事例で検証し、運用エネルギー84.7%削減、材料効率20.1%向上、ネットゼロ達成を実証。気候条件による断熱要件の差も定量化。
English
This paper proposes an AI-driven generative design framework integrating machine learning surrogates with NSGA-III and PSO to optimize building renovation for net-zero performance. Validated on 12 European case studies, it achieves 84.7% operational energy reduction and 20.1% material efficiency gains while maintaining high accuracy and 500-850x simulation speedup.
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
日本の建築ストック改修やZEB化に応用可能なAI最適化手法を提示。SSBJや省エネ基準対応の設計プロセス効率化に寄与し、建設業界の脱炭素投資判断を支援する。
In the global GX context
This framework offers a scalable AI-driven approach to building decarbonization, relevant to global net-zero targets and ESG disclosure. It demonstrates how ML can accelerate renovation design, supporting climate transition in the built environment.
👥 読者別の含意
🔬研究者:AI最適化と建築エネルギー性能の統合手法、サロゲートモデルの精度と速度のトレードオフに関する知見。
🏢実務担当者:改修設計の初期段階でエネルギー削減と材料効率を同時最適化するツールとして活用可能。
🏛政策担当者:建築部門の脱炭素政策におけるAI活用の有効性を示すエビデンス。
📄 Abstract（原文）
The construction sector accounts for around 40% of global energy usage and surpasses 36% of carbon emissions, highlighting the urgent need for improved renovation strategies. This research presents an AI-enabled generative design optimization framework that facilitates concurrent multi-objective optimization of architectural design, structural efficiency, and energy performance. The framework employs a 20-variable parametric design space and integrates a hybrid NSGA-III, a reference-point-based many-objective evolutionary algorithm with particle swarm optimization. Machine-learning surrogate models accelerate physics-based simulations by 500–850 times while maintaining prediction accuracy above 95%. The framework is validated through twelve renovation case studies comprising eleven residential and one office building spanning seven European countries, sourced from IEA SHC Task 37 and Passivhaus Institut databases, and calibrated to ASHRAE Guideline 14 standards (CVRMSE ≤ 18.6% across all buildings). The results evidence average reductions of 84.7% in operational energy consumption and enhancements of 20.1% in material efficiency, while consistently attaining a net-zero annual energy balance. Climate conditions significantly influence optimal insulation requirements, with a 37% difference between continental and Mediterranean regions. This study presents a scalable and computationally efficient method for AI-driven renovation design, overcoming the constraints of sequential approaches and facilitating substantial decarbonization of the built environment.
🔗 Provenance — このレコードを発見したソース
- crossref https://doi.org/10.3390/su18168273first seen 2026-08-13 05:55:05
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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。