Prediction of Cooling Load of An Energy Station based on GA-SVR

3rd International Symposium on Resource Exploration and Environmental Science

生态环境科学

Zhao, Dazhou^1^2 ; Zhang, Weibo^3 ; Zhang, Zhongping^1^2 ; Yang, Fan^3

Huadian Electric Power Research Institute Co. Ltd, Hangzhou, China^1

Zhejiang Provincial Key Laboratory of Energy Storage and Building Energy-Saving Technology, Hangzhou, China^2

China Huadian Corporation LTD. Tianjin Company, Tianjin, China^3

关键词: Average absolute error;    Average relative error;    Dry bulb temperature;    GA (genetic algorithm);    Maximum absolute error;    Maximum relative errors;    Output parameters;    Research object;   

Others  :  https://iopscience.iop.org/article/10.1088/1755-1315/300/4/042007/pdf
DOI  :  10.1088/1755-1315/300/4/042007

学科分类：环境科学（综合）

来源: IOP

【 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 %.