Electricity Load Forecasting Method based on Orthogonal Wavelet and Long Short-term Memory Neural Networks

Due to the fact that the volatility and periodicity generated by electrical load data affect the accuracy of power load forecasting, a electric load forecasting method based on Orthogonal Wavelet Transform-Long Short-Term Memory (abbr. OWT-LSTM) was proposed. Firstly, the orthogonal wavelet transform of electric load series was performed to eliminate the volatility of load data, then the LSTM neural network was utilized to conduct the modeling and training of all scales load series after orthogonal wavelet decomposition, and through the forecasting results of all series the forecasting results the prediction reconstruction was conducted to obtain final load forecasting results. Experimental results show that through the verification by consumers’ load data set, the forecasting performance of the proposed method is evidently better than other models, and it possesses better forecasting accuracy an stability