Short-term cooling load prediction for office buildings based on feature selection scheme and stacking ensemble model

Purpose The purpose of this paper is to target on designing a short-term load prediction framework that can accurately predict the cooling load of office buildings. Design/methodology/approach A feature selection scheme and stacking ensemble model to fulfill cooling load prediction task was proposed. Firstly, the abnormal data were identified by the data density estimation algorithm. Secondly, the crucial input features were clarified from three aspects (i.e. historical load information, time information and meteorological information). Thirdly, the stacking ensemble model combined long short-term memory network and light gradient boosting machine was utilized to predict the cooling load. Finally, the proposed framework performances by predicting cooling load of office buildings were verified with indicators. Findings The identified input features can improve the prediction performance. The prediction accuracy of the proposed model is preferable to the existing ones. The stacking ensemble model is robust to weather forecasting errors. Originality/value The stacking ensemble model was used to fulfill cooling load prediction task which can overcome the shortcomings of deep learning models. The input features of the model, which are less focused on in most studies, are taken as an important step in this paper.

Automated summary

This study proposes a short-term load prediction framework for accurately predicting cooling load of office buildings using a feature selection scheme and stacking ensemble model. The identified input features improve prediction performance and the model shows preferable accuracy compared to existing ones. The stacking ensemble model is also robust to weather forecasting errors.