| Title |
Comparative Analysis of Multivariate Machine Learning Models for Predicting Annual Energy Consumption in Elementary School Buildings |
| Authors |
정창헌(Cheong, Chang Heon) ; 황석호(Hwang, Seok-Ho) ; 김지영(Kim, Jiyeong) |
| DOI |
https://doi.org/10.5659/JAIK.2026.42.8.283 |
| Keywords |
Elementary school; Energy consumption forecasting; Machine learning; Gradient boosting; Multivariate modeling |
| Abstract |
This study aims to identify suitable machine learning models for predicting the annual energy consumption of elementary school buildings
and to examine explanatory variables that improve prediction accuracy. Energy consumption data from 2016 and 2017 were used for training,
while 2018 data were used for validation. The models evaluated included Linear Regression, Neural Networks, Random Forest, and Gradient
Boosting, with the number of explanatory variables ranging from 4 to 10. Among the models, the Random Forest model with 10 variables
showed the best performance, achieving a MAPE of 22.4 percent and an R² value of 0.821. Annual water consumption was found to have a
significant effect on improving prediction accuracy, particularly in buildings with high energy use. Although further improvements are needed
before the model can be applied to policy decisions, it may serve as a useful reference for identifying unusual energy consumption patterns
in school buildings. Future studies could improve the model by optimizing parameters, incorporating more detailed local climate data, and
including variables that reflect the specific operational characteristics of schools. |