Gradient boosting machine for modeling the energy consumption of commercial buildings

Gradient boosting machine for modeling the energy consumption of commercial buildings

TitleGradient boosting machine for modeling the energy consumption of commercial buildings
Publication TypeJournal Article
Year of Publication2018
AuthorsSamir Touzani, Jessica Granderson, Samuel Fernandes
JournalEnergy and Buildings
ISSN0378-7788
Keywordsbaseline energy modeling, energy efficiency, Gradient boosting machine, Machine learning, savings measurement and verification., statistical regression
Abstract

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.

DOI10.1016/j.enbuild.2017.11.039
LBNL Report Number

LBNL-2001097