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

Wenzhong Gao (Merchant Marine College, Shanghai Maritime University, Shanghai, China)
Xingzong Huang (Merchant Marine College, Shanghai Maritime University, Shanghai, China)
Mengya Lin (Merchant Marine College, Shanghai Maritime University, Shanghai, China)
Jing Jia (Merchant Marine College, Shanghai Maritime University, Shanghai, China)
Zhen Tian (Merchant Marine College, Shanghai Maritime University, Shanghai, China)

Engineering Computations

ISSN: 0264-4401

Article publication date: 2 February 2022

Issue publication date: 3 May 2022

1

Abstract

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.

Keywords

Acknowledgements

The authors acknowledge the financial support of Shanghai Municipal Science and Technology Commission [18040501800]; National Natural Science Foundation of China [51706129].

Citation

Gao, W., Huang, X., Lin, M., Jia, J. and Tian, Z. (2022), "Short-term cooling load prediction for office buildings based on feature selection scheme and stacking ensemble model", Engineering Computations, Vol. 39 No. 5, pp. 2003-2029. https://doi.org/10.1108/EC-07-2021-0406

Publisher

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Emerald Publishing Limited

Copyright © 2022, Emerald Publishing Limited

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