Data-driven cross-regional climate zone classification for building codes: A future-compatible framework

作者：Kanxuan He, H Guo · 发表于：Springer Link (Chiba Institute of Technology) · 年份：2026 · DOI：10.1051/e3sconf/202671610011/pdf · 研究领域：Building Energy and Comfort Optimization、Urban Heat Island Mitigation、Wind and Air Flow Studies

Climate zone classification (CZC) provides a foundational layer for building energy codes and performance standards. However, nation-specific classification schemes pose challenges to cross-border building design and construction, as well as to the alignment of performance criteria in fields such as thermal comfort and energy efficiency. Traditional climate classification systems, exemplified by the Köppen-Geiger (KG) scheme, are mainly derived from ecological and environmental characteristics, and are therefore not tailored to the requirements of building regulations. Accelerating climate change further challenges the static nature of traditional schemes with higher variability and more intensive and frequent extremes. This study proposes a data-driven CZC framework that integrates spatial heterogeneity and temporal adaptability, enabling both historically grounded and future-oriented climate zoning. The framework constructs a unified weather feature space using long-term historical sequences consolidated from ISD, ERA5-Land, and NSRDB datasets. From these sequences, we derive both Typical Meteorological Year-based features that characterize mean climatic conditions and extreme-event-based features that captures variability and tail risks. These features are then clustered through K-means to identify climate groups. Cluster quality is evaluated using a suite of EnergyPlus-simulated metrics, ensuring that zones reflect not only climatic similarity but also performance relevan...