A microclimate-based framework for multi-objective optimization of residential building design
住宅設計の多目的最適化のための微気候ベースのフレームワーク (AI 翻訳)
Wen Zhang, Bin Guo, Bo Yang, Lexin Zhang, Yutong He, Wei Zhao
🤖 gxceed AI 要約
日本語
本研究は、微気候シミュレーション（ENVI-METおよびGrasshopper）と非支配的ソート遺伝的アルゴリズムIIを統合した多目的最適化モデルを開発。中国河北省の事例研究により、室内温度を2〜3°C低減、エネルギー消費を15〜20％削減し、建設コストは5〜8％の増加にとどまることを実証。持続可能で気候レジリエントな住宅設計のための再現可能なフレームワークを提供。
English
This study develops a synergistic optimization model integrating microclimate simulations (ENVI-MET and Grasshopper) with non-dominated sorting genetic algorithm II to simultaneously balance thermal comfort, energy consumption, and economic cost. A case study in Hebei Province, China demonstrates 2–3 °C indoor temperature reduction, 15%–20% energy savings, with only 5%–8% construction cost increase. Provides a robust, replicable framework for sustainable residential building design.
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
本論文は中国河北省を対象としているが、建築セクターの脱炭素化とエネルギー効率向上の具体的な手法として、日本の建築設計や省エネ基準にも応用可能な知見を提供する。特に、複数の目的（快適性、エネルギー、コスト）を同時に最適化するアプローチは、日本におけるZEB（ネット・ゼロ・エネルギー・ビル）推進や、SSBJの気候関連開示における建物性能の評価にも示唆を与える可能性がある。
In the global GX context
This paper presents a multi-objective optimization framework for building design that directly addresses energy consumption and carbon emissions, relevant to global efforts like the UN Sustainable Development Goals 11 and 13. While the case study is in China, the methodology is transferable and contributes to the body of knowledge on building performance optimization, which informs corporate climate disclosure (e.g., TCFD/ISSB) regarding built environment impacts.
👥 読者別の含意
🔬研究者:Useful for researchers in building performance simulation and optimization, providing a validated multi-objective framework.
🏢実務担当者:Building designers and architects can apply this framework to balance thermal comfort, energy savings, and cost in residential projects.
🏛政策担当者:Policymakers focusing on building energy codes and carbon reduction targets can consider this approach for updating standards.
📄 Abstract（原文）
The building sector accounts for 40% of global energy consumption and 39% of carbon emissions, with climate change-induced extreme weather exacerbating energy demands and indoor habitability. Addressing this challenge is crucial for achieving the United Nations Sustainable Development Goals 11 and 13. However, existing research predominantly focuses on single-objective optimizations, lacking systematic multi-objective approaches. This study develops a synergistic optimization model integrating microclimate simulations (ENVI-MET and Grasshopper) with non-dominated sorting genetic algorithm II to simultaneously balance thermal comfort, energy consumption, and economic cost. A case study in Hebei Province, China—a temperate monsoon climate region with significant urbanization challenges—demonstrates compelling results: 2–3 °C indoor temperature reduction, 15%–20% energy savings, with only 5%–8% construction cost increase. This research provides a robust, replicable framework empowering architects and planners to optimize sustainable and climate-resilient residential building design.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.1063/5.0310440first seen 2026-05-20 05:17:09
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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。