Short-Term Mosques Load Forecast Using Machine Learning and Meteorological Data  

作　　者：Musaed Alrashidi 

机构地区：[1]Department of Electrical Engineering,College of Engineering,Qassim University,Buraidah,Saudi Arabia

出　　处：《Computer Systems Science & Engineering》2023年第7期371-387,共17页计算机系统科学与工程（英文）

基　　金：The author extends his appreciation to the Deputyship for Research&Innovation,Ministry of Education and Qassim University,Saudi Arabia for funding this research work through the Project Number(QU-IF-4-3-3-30013).

摘　　要：The tendency toward achieving more sustainable and green buildings turned several passive buildings into more dynamic ones.Mosques are the type of buildings that have a unique energy usage pattern.Nevertheless,these types of buildings have minimal consideration in the ongoing energy efficiency applications.This is due to the unpredictability in the electrical consumption of the mosques affecting the stability of the distribution networks.Therefore,this study addresses this issue by developing a framework for a short-term electricity load forecast for a mosque load located in Riyadh,Saudi Arabia.In this study,and by harvesting the load consumption of the mosque and meteorological datasets,the performance of four forecasting algorithms is investigated,namely Artificial Neural Network and Support Vector Regression(SVR)based on three kernel functions:Radial Basis(RB),Polynomial,and Linear.In addition,this research work examines the impact of 13 different combinations of input attributes since selecting the optimal features has a major influence on yielding precise forecasting outcomes.For the mosque load,the(SVR-RB)with eleven features appeared to be the best forecasting model with the lowest forecasting errors metrics giving RMSE,nRMSE,MAE,and nMAE values of 4.207 kW,2.522%,2.938 kW,and 1.761%,respectively.

关 键 词：Big data harvesting mosque load forecast data preprocessing machine learning optimal features selection 

分 类 号：TP39[自动化与计算机技术—计算机应用技术]