Prediction of room temperature in Trombe solar wall systems using machine learning algorithms  

作　　者：Seyed Hossein Hashemi Zahra Besharati Seyed Abdolrasoul Hashemi Seyed Ali Hashemi Aziz Babapoor 

机构地区：[1]Petroleum Systems Engineering,Faculty of Engineering and Applied Science,University of Regina,Regina,SK S4S 0A2,Canada [2]Department of Chemical Engineering,University of Mohaghegh Ardabili,Ardabil 13131-56199,Iran [3]Department of Chemistry,School of Science,Alzahra University,Tehran 1993893973,Iran [4]Computer Engineering,Gachsaran Oil and Gas Exploitation Company,Gachsaran 7581873849,Iran [5]Architectural Engineering,Shiraz Islamic Azad University,Shiraz 7473171987,Iran

出　　处：《Energy Storage and Saving》2024年第4期243-249,共7页储能与节能(英文)

摘　　要：A Trombe wall-heating system is used to absorb solar energy to heat buildings.Different parameters affect the system performance for optimal heating.This study evaluated the performance of four machine learning algorithms—linear regression,k-nearest neighbors,random forest,and decision tree—for predicting the room temperature in a Trombe wall system.The accuracy of the algorithms was assessed using R^(2)and root mean squared error(RMSE)values.The results demonstrated that the k-nearest neighbors and random forest algorithms exhibited superior performance,with R^(2)and RMSE values of 1 and 0.In contrast,linear regression and decision tree showed weaker performance.These findings highlight the potential of advanced machine learning algorithms for accurate room temperature prediction in Trombe wall systems,enabling informed design decisions to enhance energy efficiency.

关 键 词：Trombe wall Solar energy Thermal storage wall Machine learning algorithms 

分 类 号：TN9[电子电信—信息与通信工程]