Optimization of short-term power load prediction model based on VMD-SSA-RF algorithm
In response to the problem of poor accuracy in short-term electricity load forecasting models,this paper proposes a Short-term power load forecasting model that utilizes the variational mode decomposition(VMD)tech-nique to extract deep features from short-term load data,followed by optimizing the hyperparameters of the Random Forest(RF)load forecasting model using the sparrow search algorithm(SSA).Firstly,in the data processing part,VMD is used to decompose the load data to obtain multiple modal components,and the decomposed modal components are analyzed,and the modal components that are seriously affected by noise and cause excessive wave-form fluctuation are merged to reduce the calculation cost of the model.Then,the sparrow search algorithm is used to optimize the hyperparameters of the random forest prediction model.The optimal prediction model is constructed for the multiple modal components obtained after VMD decomposition,and their results are reconstructed to obtain the final prediction outcomes.Through the analysis of examples,it is verified that the proposed prediction model has higher prediction accuracy than the commonly used intelligent prediction model.
万方数据
维普
