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Title: Gradient boosting machine for modeling the energy consumption of commercial buildings

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 predictionmore » 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.« less

Authors:
 [1];  [1];  [1]
  1. Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Publication Date:
Research Org.:
Lawrence Berkeley National Lab. (LBNL), Berkeley, CA (United States)
Sponsoring Org.:
USDOE Office of Energy Efficiency and Renewable Energy (EERE), Building Technologies Office (EE-5B)
OSTI Identifier:
1439233
Grant/Contract Number:  
AC02-05CH11231
Resource Type:
Journal Article: Accepted Manuscript
Journal Name:
Energy and Buildings
Additional Journal Information:
Journal Volume: 158; Journal Issue: C; Related Information: © 2017 Elsevier B.V.; Journal ID: ISSN 0378-7788
Publisher:
Elsevier
Country of Publication:
United States
Language:
English
Subject:
32 ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION

Citation Formats

Touzani, Samir, Granderson, Jessica, and Fernandes, Samuel. Gradient boosting machine for modeling the energy consumption of commercial buildings. United States: N. p., 2017. Web. doi:10.1016/j.enbuild.2017.11.039.
Touzani, Samir, Granderson, Jessica, & Fernandes, Samuel. Gradient boosting machine for modeling the energy consumption of commercial buildings. United States. doi:10.1016/j.enbuild.2017.11.039.
Touzani, Samir, Granderson, Jessica, and Fernandes, Samuel. Sun . "Gradient boosting machine for modeling the energy consumption of commercial buildings". United States. doi:10.1016/j.enbuild.2017.11.039.
@article{osti_1439233,
title = {Gradient boosting machine for modeling the energy consumption of commercial buildings},
author = {Touzani, Samir and Granderson, Jessica and Fernandes, Samuel},
abstractNote = {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.},
doi = {10.1016/j.enbuild.2017.11.039},
journal = {Energy and Buildings},
number = C,
volume = 158,
place = {United States},
year = {Sun Nov 26 00:00:00 EST 2017},
month = {Sun Nov 26 00:00:00 EST 2017}
}

Journal Article:
Free Publicly Available Full Text
This content will become publicly available on November 26, 2018
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Cited by: 1 work
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