自律型建築外皮に向けて：将来の都市建築におけるAIによる気候適応

Unofficial AI-generated summary based on the public title and abstract. Not an official translation.

🔬研究者:AIと建築外皮の統合による省エネ効果の定量化と、デジタルツイン・予測制御の研究動向を把握できる。

🏢実務担当者:建築設計・不動産開発において、AI制御による省エネ・気候適応の導入可能性を検討する際の参考になる。

🏛政策担当者:建築分野の脱炭素政策や省エネ基準の強化に向けて、AI技術の活用余地を示す。

Purpose: Urban buildings are increasingly exposed to climate-related challenges including overheating, rising cooling demand, energy price volatility, and changing occupancy patterns. Globally, buildings are responsible for nearly 30% of final energy consumption and approximately 26% of energy-related greenhouse gas emissions, while cooling demand in cities is projected to increase significantly under future climate scenarios. Conventional building envelopes remain largely static and are unable to dynamically respond to environmental and operational conditions. This contribution explores the emerging concept of autonomous building envelopes, where artificial intelligence, sensors, and adaptive façade technologies are integrated to enable climate-responsive urban buildings. Methodology: The paper presents a conceptual review and synthesis of recent developments in AI-enabled building systems, adaptive façades, smart windows, dynamic shading technologies, and predictive control approaches. Existing technologies and operational concepts are analysed to develop an integrated framework for autonomous building envelope systems in future urban environments, considering interactions between building envelopes, HVAC systems, occupancy behaviour, and renewable energy integration. Main findings: Current adaptive façade technologies are often implemented as isolated subsystems with limited coordination between envelope components, HVAC systems, occupancy behaviour, and grid interaction. Recent studies indicate that AI-enabled predictive control strategies can reduce building cooling and heating demand by approximately 10–30% depending on climate conditions, control complexity, and building typology. AI-driven supervisory control, digital twins, and predictive optimisation approaches have the potential to transform building envelopes from passive structures into intelligent and climate-adaptive systems capable of dynamically balancing thermal comfort, daylight, energy demand, and renewable energy integration. Recommendations: Future research should focus on integrated AI-envelope-HVAC architectures, scalable digital twin frameworks, occupant-centric control strategies, and interoperable sensing systems to support the development of resilient, low-carbon, and grid-interactive urban buildings.

• Zenodo https://zenodo.org/records/22232000first seen 2026-09-02 04:25:20 · last seen 2026-09-03 04:37:41