期刊名称：IOP Conference Series: Earth and Environmental Science
印刷版ISSN：1755-1307
电子版ISSN：1755-1315
出版年度：2019
卷号：300
期号：4
页码：1-6
DOI：10.1088/1755-1315/300/4/042007
出版社：IOP Publishing
摘要：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 %.
