Machine learning approaches for estimating building energy consumption. Issue 5 (April 2020)

• Record Type: Journal Article
• Authors: Liu, Liangyu; Liu, Ningyi; Zhang, Yilin; Li, Yumeng; Rui, Xiaobo; Yang, Zi
• Is Part Of: IOP conference series. Volume 474:Issue 5(2020)
• Publication Date: 2020-04
• Subjects: Earth sciences -- Periodicals; Environmental sciences -- Congresses; Environmental sciences -- Periodicals; 550.5
• DOI: 10.1088/1755-1315/474/5/052072
• Languages: English
• ISSNs: 1755-1307

Abstract

Abstract: Using building data and corresponding weather conditions provided by ASHRAE, a statistical method that carefully measures features and applies both linear regression and gradient boosting machine models to predict and analyse building energy consumption was developed. Comparison of the predicted and actual energy usage indicates our model can predict energy consumption within an acceptable error range. Such statistical model has the potential to be widely used to monitor energy consumption and measure energy savings for various kinds of buildings in the future. If additional data is available, this method will be more widely applicable to other sectors such as industrial facilities.