An Ensemble Learning Approach for Accurate Energy Load Prediction in Residential Buildings

IEEE Access

Abdu Gumaei¹  Mabrook Al-Rakhami²  Atif Alamri²  Mohammad Mehedi Hassan²  Ahmed Alsanad³ 

[1] Department of Computer Science, King Saud University, Riyadh, Saudi Arabia;Department of Information Systems, Chair of Pervasive and Mobile Computing (CPMC), College of Computer and Information Sciences, King Saud University, Riyadh, Saudi Arabia;Department of Information Systems, King Saud University, Riyadh, Saudi Arabia;

关键词: Building energy loads;    residential buildings;    prediction;    ensemble learning;    extreme gradient boosting;   

DOI  :  10.1109/ACCESS.2019.2909470

来源: DOAJ

【 摘 要 】

Reducing energy loads while maintaining the degree of hotness and coldness plays an essential role in designing energy-efficient buildings. Some previous methods have been proposed for predicting building energy loads using traditional machine learning methods. However, these traditional methods suffer from overfitting problems, which leads to inaccurate prediction results. To achieve high accuracy results, an ensemble learning approach is proposed in this paper. The proposed approach uses an extreme gradient boosting (XGBoost) algorithm to avoid overfitting problems and builds an efficient prediction model. An extensive experiment is conducted on a selected dataset of residential building designs to evaluate the proposed approach. The dataset consists of 768 samples of eight input attributes (overall height, relative compactness, wall area, surface area, roof area, glazing area distribution, glazing area, and orientation) and two output responses (cooling load (CL) and heating load (HL)). The experimental results prove that the proposed approach achieves the highest prediction performance, which will help building managers and engineers make better decisions regarding building energy loads.

【 授权许可】

Unknown