Open AccessPrediction of Cooling Load of An Energy Station based on GA-SVR
Author(s) -
Dazhou Zhao,
Weibo Zhang,
Zhongping Zhang,
Fan Yang
Publication year - 2019
Publication title -
iop conference series. earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/300/4/042007
Subject(s) - approximation error , bulb , research object , moment (physics) , value (mathematics) , energy (signal processing) , mean absolute error , mean absolute percentage error , mathematics , cooling load , genetic algorithm , statistics , mean squared error , control theory (sociology) , algorithm , computer science , mathematical optimization , physics , artificial intelligence , thermodynamics , control (management) , classical mechanics , regional science , air conditioning , horticulture , biology , geography
Taking an energy station as the research object, the external dry bulb temperature and load values at t-1, t-2, t-3 moments were selected as input parameters, and the load value at t moment was used as output parameters to establish the SVR(Support Vector Regress)cooling load prediction model, the key parameters of SVR are optimized by GA(Genetic Algorithm).The results show that the maximum absolute error between the predicted value and the actual value is 4.83 GJ/h, the maximum relative error is 9.2 %, the average absolute error is 1.25 GJ/h, and the average relative error is 2.4 %.