Improved BWO-TimesNet Short-term Heat Load Forecasting Model Based on SVMD
Accurate and efficient heat load forecasting is very important to ensure the stable operation of thermal system and rational planning of thermal resources.In order to improve the accuracy of heat load forecasting,a TimesNet short-term heat load forecasting model based on successive variational mode decomposition(SVMD)and an improved beluga optimization algorithm(IBWO)is proposed.Firstly,SVMD is used to decompose the original heat load data,and several stable and regular modal components are obtained after removing noise.Secondly,according to the characteristics of each modal component,the appropriate feature is selected as the input.Then,three strategies are introduced to improve the Beluga optimization algorithm,and the IBWO-TimesNet prediction model is established.Finally,the prediction performance of the model is evaluated in detail by an example.The results show that MAE,RMSE and R2 of SVMD-IBWO-TimesNet model are 0.647,1.190 and 99.1％,respectively.Compared with other mainstream prediction models,this model has higher prediction accuracy.At the same time,the SVMD-IBWO-TimesNet model can still effectively predict the heat load and has strong generalization ability when the training samples are reduced.Therefore,the validity of the proposed model is verified,and the reference is provided for the precise regulation and control of the heating load of the thermal system.
万方数据
维普
