Novel building energy performance-based climate zoning enhanced with spatial constraint

Abstract

Existing buildings' climate zoning approaches often overlook the incorporation of building energy usage data, leading to discrepancies between climate zoning classifications and the ever-increasing demands for energy efficiency in modern construction practices. This research proposes a novel spatially constrained approach that incorporates multivariate clustering and building energy needs indicators to create a climate classification aligned with buildings' energy performance patterns. The study focuses on Kazakhstan as a case study, where buildings experience distinct climatic conditions across different regions. A spatial constraint was used to enhance hierarchical and k-means clustering methods, which were applied to the space heating and cooling energy needs of the most typical building archetypes. The quality of clustering results was evaluated using uniqueness and dispersion indicators. Furthermore, The Adjusted Rand Index was introduced to compare the proposed method with the ASHRAE and the official buildings' climate map of Kazakhstan. The existing climate maps failed to match the patterns of building energy performance. Overall, the proposed spatially constrained method exhibits promising results and offers optimized buildings' climate zoning supported by buildings' energy performance data and rigorous measures of clustering quality.