Energy and Operational-Carbon Coupling for Sustainable Beijing Hotel Design: Parametric EnergyPlus Simulation and Machine-Learning Surrogate Analysis
持続可能な北京ホテル設計のためのエネルギーと運用炭素の連成：パラメトリックEnergyPlusシミュレーションと機械学習サロゲート解析 (AI 翻訳)
Yuxiang Xiao, Xiong Zheng
🤖 gxceed AI 要約
日本語
北京のホテル建築を対象に、EnergyPlusによるパラメトリックシミュレーションと機械学習サロゲートを組み合わせ、運用エネルギーと炭素排出の連成を解析した。20,000ケースから4,640の成功シミュレーションを抽出し、主要な設計因子を特定。Poly3-RidgeCVモデルが高い適合度を示し、EUIとOCEIの相関は強いが完全には一致しないことを明らかにした。初期設計段階での低炭素代替案の比較に貢献する。
English
This study develops a reproducible EnergyPlus-based framework combining parametric simulation, interpretable sensitivity analysis, and machine learning surrogates to analyze operational energy and carbon coupling for Beijing hotels. From 20,000 candidates, 4,640 simulations were retained; Poly3-RidgeCV achieved high fidelity (R2=0.9976). EUI and OCEI are strongly correlated (r=0.954) but not interchangeable, with 71.8% overlap in top-10% low cases. The framework supports early-stage comparison of energy-efficient, low-carbon design alternatives.
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
日本の建築分野では、省エネ法やZEB普及に加え、運用段階の炭素排出削減が重要視されている。本手法は設計初期段階でのエネルギー・炭素の同時評価を可能にし、日本の建築設計実務やカーボンニュートラル政策に示唆を与える。
In the global GX context
Globally, the building sector is a major source of emissions, and operational carbon accounting is critical for net-zero targets. This framework demonstrates a reproducible approach combining simulation and ML for early-stage design optimization, relevant to TCFD/ISSB-aligned disclosure and sustainable building standards.
👥 読者別の含意
🔬研究者:Provides a reproducible ML-surrogate framework for building energy-carbon coupling analysis, useful for further methodological development.
🏢実務担当者:Offers a practical tool for early-stage hotel design to compare energy-efficient and low-carbon alternatives, aiding sustainability reporting.
🏛政策担当者:Highlights the importance of operational carbon accounting in building codes and the potential of ML surrogates for policy evaluation.
📄 Abstract（原文）
Improving the operational energy and carbon performance of hotel buildings is an important environmental dimension of sustainable building design. This study develops a reproducible EnergyPlus-based framework that combines parametric simulation, interpretable sensitivity analysis, machine learning surrogates, and carrier-resolved operational carbon accounting for Beijing hotel buildings. From 20,000 Latin hypercube candidates, input-side physical and functional screening retained 4640 successful EnergyPlus simulations. The simulated EUI mean was 140.6 kWh/(m2·a), 14.3% above the published Beijing hotel mean; surrogate performance is therefore interpreted as fidelity to the simulator rather than direct measured-building prediction. SRC with bootstrap uncertainty and a SHAP cross-check identified the main domestic-hot-water, building-form, and HVAC drivers. Of 17 models, Poly3-RidgeCV achieved the highest held-out fidelity (R2 = 0.9976; RMSE = 1.72 kWh/(m2·a)). Baseline OCEI averaged 48.20 kgCO2e/(m2·a); EUI and OCEI were strongly correlated (r = 0.954) but not interchangeable, with 71.8% overlap between the top-10% low-EUI and top-10% low-OCEI cases. Emission-factor scenarios showed robust but non-static energy-carbon coupling. The framework supports early-stage comparison of energy-efficient alternatives with comparatively lower operational carbon emissions within the stated accounting boundary, contributing to sustainable hotel design without constituting a whole-life or measured-building sustainability assessment.
🔗 Provenance — このレコードを発見したソース
- openalex https://doi.org/10.3390/su18157697first seen 2026-07-31 06:19:21
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