Multi-channel Short-Term Power Load Prediction Model Based on VMD Data Decomposition
In response to the randomness and instability of power load data,as well as the difficulty of traditional prediction models in accurately capturing local and global information of the data,this paper proposes a VMD-AdaBoost-CNN-CBAM+LSTM-ATT(Hereinafter referred to as VACCLA)multi-channel short-term power load prediction model based on variational mode decomposition(VMD).Firstly,this method utilizes VMD to decompose the original load data into feature modal components representing different scales,in order to reduce the instability of the original sequence.Then,multiple features are introduced into the decomposed modal components,and the AdaBoost decision tree optimization algorithm is used to weight and process each dataset.By amplifying or compressing the data features,the model focuses more on some important data features,Finally,use the CNN-CBAM+LSTM-ATT model for load forecasting.Through comparative experiments with multiple prediction models,the experimental results show that this model has better prediction performance compared to traditional prediction methods.
power load predictionvariational modal decompositionAdaBoost decision treeVACCLA
NETL
NSTL
万方数据
