다변수 기반 기계학습을 통한 초등학교 연간에너지 소비량 예측 모델 성능비교

초록

This study aims to identify suitable machine learning models for predicting the annual energy consumption of elementary school buildingsand 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 GradientBoosting, with the number of explanatory variables ranging from 4 to 10. Among the models, the Random Forest model with 10 variablesshowed the best performance, achieving a MAPE of 22.4 percent and an R² value of 0.821. Annual water consumption was found to have asignificant effect on improving prediction accuracy, particularly in buildings with high energy use. Although further improvements are neededbefore the model can be applied to policy decisions, it may serve as a useful reference for identifying unusual energy consumption patternsin school buildings. Future studies could improve the model by optimizing parameters, incorporating more detailed local climate data, andincluding variables that reflect the specific operational characteristics of schools.

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