Short-Term Power Load Forecasting Based on MIC-ResNet-LSTM-BP
Reasonable dispatch of electric energy is an important issue related to people's livelihood,and reasona-ble electric energy dispatch is inseparable from accurate load forecasting.In order to effectively improve the accuracy of load forecasting,this paper proposes a short-term power load forecasting method based on MIC-ResNet-LSTM-BP to improve the accuracy of load forecasting for the next 1 day and 3 days.First,the 6-dimensional load characteristic data were collected,and the maximum information coefficient(MIC)was used to analyze the correlation between each influencing factor and the load,so as to carry out feature selection;secondly,residual network(ResNet)was used to extract features from the data;then,the reconstructed data was input into the short and long duration memory network(LSTM)to mine the temporal features of the data;finally,the Dropout layer was used to increase the generalization ability of the model,and the data features of BP Neural Network Science(BPNN)were improved and the Adam opti-mizer was used to train the model.Compared with BPNN,KNN,LSTM and LSTM-BPNN,the accuracy of this model in the field of load prediction is verified.
Maximum information coefficientLoad forecastingResidual networkShort and long duration mem-ory networkNeural network
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