Data-driven characterization of cooling needs in a portfolio of co-located commercial buildings

Abstract

Summary: The increasing cooling needs in commercial buildings, exacerbated by climate change, warrant immediate attention. These buildings, characterized by their long lifespans and slow stock turnover, change consumption over time. This study develops simple, interpretable data-driven models using weather- and occupancy-related features to analyze the cooling in different types of co-located buildings. Over five years, our models effectively predict the cooling load across buildings with R-squared values of 81%–87%. Factoring out geography-driven differences, we identify strong heterogeneity within and across different buildings. The average estimated base load cooling varies between 0.50 and 4.4 MJ/m2/day across buildings, with healthcare facilities exhibiting the highest loads and residences the lowest. Consumption increases by 7.6%–9.8% for every 1°C increase in mean daily outside temperature, with up to 27% reductions in offices on weekends. These insights enable diagnoses of inefficiencies, post-retrofitting performance tracking, and proactive planning for climate-related impacts.

Keywords

• energy modeling
• energy systems
• engineering