BEEM: Data-driven building energy benchmarking for Singapore

Building energy use benchmarking is the process of measuring the energy performance of buildings relative to their peer group for creating awareness and identifying energy-saving opportunities. In this paper, we present the design and implementation of BEEM, a data-driven energy use benchmarking system for buildings in Singapore. The peer groups for comparison are established using a public energy disclosure data set. We use an ensemble tree algorithm for accurately modeling building energy use and for identifying the most influential factors. Our models reduce the prediction error from 24.39% to 6.04%, on average, when compared to the baseline linear regression models, which were used in the previous energy efficiency labeling program in Singapore, and outperforms ten other recent models. Using the prototype implementation of BEEM, we benchmarked three building types, office (290), hotel (203), and retail (125), and compared their rating. The code repository and the accompanying data set are released as an open-source project for community use.(c) 2022 Elsevier B.V. All rights reserved.

Automated summary

This paper presents the design and implementation of BEEM, a data-driven energy use benchmarking system for buildings in Singapore. The system establishes peer groups for comparison using a public energy disclosure data set and utilizes an ensemble tree algorithm to accurately model building energy use and identify influential factors. Compared to baseline linear regression models used in the previous energy efficiency labeling program in Singapore and other recent models, our models significantly reduce prediction error. Using the prototype implementation of BEEM, we benchmarked and compared the energy performance of office, hotel, and retail buildings.