Development of an improved model to predict building thermal energy consumption by utilizing feature selection

Jihoon Jang, Joosang Lee, Eunjo Son, Kyungyong Park, Gahee Kim, Jee Hang Lee, Seung Bok Leigh

Research output: Contribution to journalArticlepeer-review

15 Citations (Scopus)


Humans spend approximately 90% of the daytime in buildings, and greenhouse gases (GHGs) emitted by buildings account for approximately 20% of total GHG emissions. As the energy consumed during building operation from a building life-cycle perspective amounts to approximately 70-90% of the total energy, it is essential to accurately predict the energy consumption of buildings for their efficient operation. This study aims to optimize a model for predicting the thermal energy consumption of buildings by (i) first extracting major variables through feature selection and deriving significant variables in addition to the collected data and (ii) predicting the thermal energy consumption using a machine learning model. Feature selection using random forest was performed, and 11 out of 17 available data were selected. The accuracy of the prediction model was significantly improved when the hour of day variable was added. The prediction model was constructed using an artificial neural network (ANN), and the improvement in the prediction accuracy was analyzed by comparing different cases of variable combinations. The ANN prediction accuracy was improved by 15% using the feature selection process compared to when all data were used as input data, and 25% coefficient of variation of the root mean square error (CVRMSE) accuracy was achieved.

Original languageEnglish
Article numberen12214187
Issue number21
Publication statusPublished - 2019

Bibliographical note

Funding Information:
Funding: This research was sponsored by the Korean Institute of Energy Technology Evaluation and Planning (KETEP) of the Republic of Korea (No. 2018201060010A).

Publisher Copyright:
© 2019 by the authors.

All Science Journal Classification (ASJC) codes

  • Renewable Energy, Sustainability and the Environment
  • Energy Engineering and Power Technology
  • Energy (miscellaneous)
  • Control and Optimization
  • Electrical and Electronic Engineering


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