Benchmarking building energy efficiency using quantile regression. (1st June 2018)

• Record Type: Journal Article
• Authors: Roth, Jonathan; Rajagopal, Ram
• Is Part Of: Energy. Volume 152(2018)
• Page Start: 866
• Page End: 876
• Publication Date: 2018-06-01
• Subjects: Quantile regression -- Energy benchmarking -- Energy efficiency -- Building performance -- Data analytics; Power resources -- Periodicals; Power (Mechanics) -- Periodicals; Energy consumption -- Periodicals; 333.7905
• DOI: 10.1016/j.energy.2018.02.108
• Languages: English
• ISSNs: 0360-5442

Abstract

Abstract: We propose a new building energy use benchmarking system to rank buildings via quantile regression. This methodology addresses several leading issues with current benchmarking practices by constructing a data-driven probabilistic model of performance, reducing outlier-effects, determining the varying effect of inputs across the distribution, and creating a theoretical maximum performance-level for each building. Influence plots constructed to examine a variable's effect on the conditional distribution can identify main drivers of energy consumption at each quantile, visually displaying any nonlinear effects on energy consumption. The methodology produces a score for each building based on efficiency, compares buildings with their constructed distribution of scores, and extracts the strongest indicators of energy use. To illustrate the model's effectiveness, we analyzed electricity consumption from a dataset containing ∼1000 buildings and found that cooling degree days and the presence of gyms, spas, and elevators were large drivers of energy use. Additionally, the number of employees per unit area had a larger effect on total energy consumption for poor performing buildings as compared to top performers. This more robust and standardized benchmarking model may improve resource allocation for energy-efficient programs, encourage competition between buildings, put pressure on poor performers, and provide insight into building energy drivers. Highlights: We propose a novel building energy benchmarking method based on a data-driven probabilistic model of performance. The system reveals changing effects of energy drivers from low to high performing buildings. We provide a framework for building managers and policymakers to interpret results. The model performance is evaluated on a dataset of ∼1000 buildings across two years.