Research on the load forecast of high-efficiency refrigeration room based on whale optimization algorithm
In order to accurately forecast the cooling load of buildings and reduce the energy consumption of the refrigeration room,a BP(Back Propogation)neural network forecasting model based on the whale optimization algorithm(WOA)is proposed in this paper.Based on the actual historical operational data of office buildings in Qingdao,four load forecasting models were established,namely BP neural network,BP neural network based on particle swarm optimization(PSO-BP),BP neural network improved by genetic algorithm(GA-BP)and BP neural network based on whale optimization algorithm(WOA-BP).A comparative analysis of the results from these four load forecasting models was conducted.The research shows that in the short-term forecasting,the maximum percentage error of WOA-BP neural network forecasting model is-15.76％,the minimum percentage error is-0.03％,and the average absolute percentage error is 6.60％.Compared with the other models,WOA-BP model has higher forecasting accuracy.
building energy savingwhale optimization algorithm(WOA)load forecastBack Propagation neural network
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