Life-cycle assessment and multi-objective optimization of natural-insulated envelopes across Iranian climates
イラン気候における自然断熱外皮のライフサイクル評価と多目的最適化 (AI 翻訳)
Peyman Naghipour, Afshin Naghipour, Tarana Bakirova
🤖 gxceed AI 要約
日本語
本研究では、イランの4つの気候地域において、自然断熱材（ストロー・ヘンプ複合材など）とパッシブ戦略を組み合わせた建物外皮の最適化を行い、ライフサイクルアセスメント（LCA）と多目的最適化（NSGA-II）を統合。従来材料と比較してCO2排出量を42–58%削減、年間エネルギー消費を25–35%削減、ライフサイクルコストを15–25%削減できることを示した。また、機械学習（ランダムフォレスト）による予測モデルも構築し、精度の高いエネルギー消費推定を実現。ゼロエネルギー建築への移行を支援する統合的フレームワークを提供した。
English
This study optimizes building envelopes with natural insulation and passive strategies across four Iranian climates using life-cycle assessment and multi-objective optimization. Results show 42-58% reduction in CO2 emissions, 25-35% reduction in annual energy consumption, and 15-25% reduction in life-cycle costs compared to conventional materials. Random Forest models accurately predict energy consumption, providing an integrated framework for net-zero energy buildings in developing regions.
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ZEHやLCCM住宅政策とも親和性が高く、自然断熱材やライフサイクル評価を統合した最適化手法は、日本の住宅・建築分野での脱炭素化に向けた参考となる。特に、機械学習を活用したエネルギー消費予測モデルは、実務での簡易評価に応用可能。
In the global GX context
Globally, this paper contributes to building decarbonization by integrating LCA, optimization, and machine learning for climate-responsive design. The findings support net-zero building initiatives and offer a replicable methodology for developing regions.
👥 読者別の含意
🔬研究者:This paper provides a comprehensive methodology combining LCA, multi-objective optimization, and machine learning for building envelope design, applicable to various climates.
🏢実務担当者:Offers practical insights on natural insulation materials and passive strategies that can reduce energy and carbon in building projects.
🏛政策担当者:Demonstrates the potential of natural insulation and integrated design for achieving net-zero targets in building codes and standards.
📄 Abstract（原文）
The building envelope plays a pivotal role in achieving sustainable energy performance, particularly in regions characterized by extreme climatic variations. This study investigates the holistic optimization of building envelopes—comprising walls, roofs, floors, and fenestrations—by integrating natural insulation materials with passive and smart design strategies. The research aims to enhance energy efficiency, reduce total CO₂ emissions, and improve occupants’ thermal comfort across four representative low-income climatic regions of Iran: Yazd (hot-arid), Tabriz (cold-dry), Rasht (temperate-humid), and Bandar Abbas (hot-humid). Addressing the existing research gap, the study extends beyond operational energy analysis by incorporating a full life cycle assessment (LCA), including embodied energy and life cycle carbon footprint. Multi-objective optimization (using NSGA-II) was performed to minimize annual energy demand, life-cycle cost (LCC), and environmental impact simultaneously. Building performance simulations were conducted using IES-VE and EnergyPlus, while LCA and economic analyses were executed via SimaPro and HOMER Pro. The results indicate that hybrid natural insulations-particularly straw–hemp composites-combined with passive strategies (dynamic shading, natural ventilation, and thermal mass enhancement) can reduce total CO₂ emissions by 42–58% compared with conventional materials. Also, the results demonstrate that the optimized design solutions can reduce annual energy consumption by approximately 25–35% compared to the baseline design, while achieving a 15–25% reduction in life-cycle costs over the building lifespan. Additionally, orientation-sensitive optimization improved thermal comfort indices (Predicted Mean Vote—PMV, Predicted Percentage of Dissatisfied—PPD) throughout the year. The developed predictive models based on machine learning (Random Forest) exhibited robust accuracy in estimating energy consumption. The findings provide an integrated framework for sustainable, low-cost, and climate-responsive envelope design, supporting the transition toward net-zero energy buildings in developing regions.
🔗 Provenance — このレコードを発見したソース
- semanticscholar https://doi.org/10.59400/be3952first seen 2026-06-29 07:21:17
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