Ultra-Short-Term Building Cooling Load Prediction Model Based on Feature Set Construction and Ensemble Machine Learning

Yan Ding,Xiangfei Kong,Zhenqin Zhang,Hao Su

doi:10.1109/access.2020.3027061

Yan Ding, Xiangfei Kong + Show 2 more

Open Access

https://doi.org/10.1109/access.2020.3027061

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Abstract

As the requirements for the optimal control of building systems increase, the accuracy and speed of load predictions should also increase. However, the accuracy of load predictions is related to not only the prediction algorithm, but also the feature set construction. Therefore, this study develops a short-term building cooling load prediction model based on feature set construction. The impacts of four different feature set construction methods—feature extraction, correlation analysis, K-means clustering, and discrete wavelet transform (DWT)—on the prediction accuracy are compared. To ensure that the effect of the feature set construction method is universal, three different prediction algorithms are used. The influences of the sample dimension and prediction time horizon on the prediction accuracy are also analysed. The prediction model is developed based on an ensemble learning algorithm utilising the cubist algorithm, and the performance of the prediction model is improved when DWT is used for constructing the feature set. Compared with other commonly used prediction models, the proposed model exhibits the best performance, with R-squared and CV-RMSE values of 99.8% and 1.5%, respectively.

Highlights

This study analysed the potential of ensemble learning models for predicting the cooling load of office buildings from the perspective of the feature set construction and the selection of prediction algorithms
(1) In terms of pre-processing, the results showed that the model based on a feature set constructed by applying discrete wavelet transform (DWT) yielded a significant improvement in prediction performance
When DWT was combined with the CA method to construct the feature set, the CA-DWT feature set could meet the 30% CV-RMSE limit

Summary

Introduction

Y. Ding et al.: Ultra-Short-Term Building Cooling Load Prediction Model globally [1]. With its recent rapid economic growth, building energy consumption in China has increased significantly. Its energy efficiency remains low compared with that in most developed countries [2]. As the construction areas in China increase, energy consumption and carbon dioxide emissions continue to increase [3]. Improvements in building energy efficiency have the potential to create immense energy and economic savings [5]. Reliable cooling load prediction results are the basis for optimised building operation strategies and effective methods to improve operation efficiency [6, 7]. Embed the load prediction model in a smart city architecture can support the development of sustainable cities[8]. Researchers have previously conducted extensive studies on building load prediction methods [9]

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