Research on Regional Short-term Load Forecasting Based on SVM-STL-LSTM
Aiming at the problems of strong randomness of time series data of regional power load,low prediction ac-curacy and poor data feature extraction ability of a single model,a combined power load forecasting model based on sup-port vector machine(SVM),STL time series decomposition method,and long short-term memory neural network(LSTM)was proposed.This model uses SVM to initially predict the power load data of a time series,and uses STL time series decomposition to decompose the residual sequence,thereby improving the stability of the residual sequence and re-ducing its randomness.Finally,the LSTM is used to correct the prediction error of the SVM.The experimental results show that this method can effectively process highly random data using error correction,which is conducive to the stabili-ty of prediction results and improving prediction accuracy.
combined modelsupport vector machineSTL time series decomposition methodlong short-term memory networkshort-term predictionerror correction
NETL
NSTL
万方数据
维普
