MC-BP Short-term Power Load Forecasting Method Considering Error Correction
Accurate short-term load forecasting is an important basis for daily grid scheduling.In response to the current problem of low precision in short-term power load forecasting,this paper proposes a correction of error rolling with MC-BP method for short-term load forecasting.Firstly,a BP load forecasting model based on the stepwise trial and error method is established,and the probability density distribution of the forecasting error is analyzed.Then,a Monte Carlo-based daily load error rolling correction strategy is constructed.Load data from a certain region during 2015-2019 was used to compare the prediction results of the CNN-BiLSTM,LSTM,and BP models.The NRMSE of the test sets for the three models were 5.97%,6.49%,and 5.5%,respectively.The error correction strategies of BP and LSTM prediction methods,linear regression method and error rolling correction method were compared.The relative change rate of NRMSE for error correction on the following day was-26.68%,-28.81%,-43.90%and-88.64%respectively.The results show that the proposed MC-BP method considering error rolling correction has good prediction performance for short-term power load forecasting.
BP neural networkMonte Carlopower load forecastingerror correctionrolling correction
万方数据
维普
