Gradient boosting machine for modeling the energy consumption of commercial buildings

Touzani, Samir; Granderson, Jessica; Fernandes, Samuel

doi:10.1016/j.enbuild.2017.11.039

Gradient boosting machine for modeling the energy consumption of commercial buildings

Journal Article · Sun Nov 26 00:00:00 UTC 2017 · Energy and Buildings

DOI:https://doi.org/10.1016/j.enbuild.2017.11.039· OSTI ID:1439233

Touzani, Samir ^[1]; Granderson, Jessica ^[1]; Fernandes, Samuel ^[1]

Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)

Accurate savings estimations are important to promote energy efficiency projects and demonstrate their cost-effectiveness. The increasing presence of advanced metering infrastructure (AMI) in commercial buildings has resulted in a rising availability of high frequency interval data. These data can be used for a variety of energy efficiency applications such as demand response, fault detection and diagnosis, and heating, ventilation, and air conditioning (HVAC) optimization. This large amount of data has also opened the door to the use of advanced statistical learning models, which hold promise for providing accurate building baseline energy consumption predictions, and thus accurate saving estimations. The gradient boosting machine is a powerful machine learning algorithm that is gaining considerable traction in a wide range of data driven applications, such as ecology, computer vision, and biology. In the present work an energy consumption baseline modeling method based on a gradient boosting machine was proposed. To assess the performance of this method, a recently published testing procedure was used on a large dataset of 410 commercial buildings. The model training periods were varied and several prediction accuracy metrics were used to evaluate the model's performance. The results show that using the gradient boosting machine model improved the R-squared prediction accuracy and the CV(RMSE) in more than 80 percent of the cases, when compared to an industry best practice model that is based on piecewise linear regression, and to a random forest algorithm.

View Accepted Manuscript (DOE)

Research Organization:: Lawrence Berkeley National Laboratory (LBNL), Berkeley, CA (United States)

Sponsoring Organization:: USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B); USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office

Grant/Contract Number:: AC02-05CH11231

OSTI ID:: 1439233

Alternate ID(s):: OSTI ID: 1496335

Journal Information:: Energy and Buildings, Journal Name: Energy and Buildings Journal Issue: C Vol. 158; ISSN 0378-7788

Publisher:: ElsevierCopyright Statement

Country of Publication:: United States

Language:: English

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