Generalized building energy and carbon emissions benchmarking with post-prediction analysis

Abstract

This study proposes a generalized building energy and carbon emissions benchmarking approach. Leveraging eleven years of real-world data from twelve U.S. cities, an advanced ensemble learning model is employed to predict building site, source energy, and carbon emissions. The generalizability is validated across seven climate zones, yielding R2 values ranging from 0.3 to 0.9. Additionally, a post-prediction analysis method is proposed that categorizes actual and predicted values into well-estimated, underestimated, and overestimated groups by an [−15%, 15%] acceptance interval (AI). This approach allows for assessing predictions related to energy, building types, and climate zones. The findings indicate the feasibility of a reliable generalized benchmarking tool. It’s been observed that energy in most underestimated groups tends to be higher than that of their overestimated counterparts. Over 63% of buildings' predictions perform within the well-estimated group by [−15%, 15%] AI.

Keywords

• Generalized energy benchmarking
• Carbon emissions
• Data-driven
• Machine learning
• Big data
• Post-prediction analysis