Surrogate model evaluation and building energy benchmarking for commercial buildings

Journal Article · Sat Jan 24 00:00:00 EST 2026 · Energy and Buildings

DOI:https://doi.org/10.1016/j.enbuild.2026.117033· OSTI ID:3017555

Chinde, Venkatesh ^[1]; Chintala, Rohit ^[1]; Kim, Janghyun ^[1]; Chapin, Alex ^[1]; Xiong, Jie ^[1]; Fleming, Katherine ^[1]; Ball, Brian L. ^[1]

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1. National Laboratory of the Rockies (NLR), Golden, CO (United States)

Building energy consumption benchmarking involves challenges associated with various energy patterns for different building types; heating, ventilating, and air-conditioning (HVAC) system types; and climates. Given significant variation in energy use patterns, accurate prediction of long-term energy use using surrogate models remains challenging. Multiple linear regression (MLR) is commonly used for building energy benchmarking because of its simple structure; however, it lacks accuracy compared to other black-box models. Although many studies have compared surrogate models and offer guidance on model selection based on metrics, they do not provide detailed analysis on improving the surrogate model accuracy. In this paper, we implement a surrogate model using polynomial ridge regression (i.e., MLR with interaction terms combined with ridge regularization) for small office and retail strip mall buildings across six HVAC system types and all climate zones, for electricity and natural gas in baseline and proposed scenarios. A simulation workflow is developed using OpenStudio^TM/EnergyPlus^TM to generate simulation data using measures over a wide range of efficiency inputs. Enhancements based on statistical insights are used for improving the model accuracy using filters, input transformations, and change points. Surrogate models achieved average coefficient of variation of the root mean squared error (CVRMSE) values of 2.17, 1.06, 2.05, and 3.26 for proposed electricity, proposed natural gas, baseline electricity, and baseline natural gas, respectively, with enhancements reducing CVRMSE by an average of 14.9% across all combinations. We provide model interpretation via Shapley additive explanations to determine which input variables most influence energy consumption and provide supportive arguments for enhancements.

• Research Organization: National Laboratory of the Rockies (NLR), Golden, CO (United States)
• Sponsoring Organization: Internal Revenue Service (IRS); USDOE Office of Critical Minerals and Energy Innovation (CMEI). Office of Innovation, Affordability and Consumer Choice (IACC). Building Technologies Office (BTO); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office
• Grant/Contract Number: AC36-08GO28308
• OSTI ID: 3017555
• Report Number(s): NLR/JA--5500-97257
• Journal Information: Energy and Buildings, Journal Name: Energy and Buildings Vol. 355; ISSN 0378-7788
• Publisher: Elsevier
• Country of Publication: United States
• Language: English

Related Subjects

32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION
building energy benchmarking
commercial building types
surrogate model