Short-term electrical load forecasting for integrated energy system based on variational mode decomposition
Aiming at the characteristics of complex and variable load and strong coupling of integrated energy system,a combined forecasting model based on variational mode decomposition(VMD),Prophet model,long-and short-term memory network(LSTM)and autoregressive integrated moving average(ARIMA)model is proposed for short-term electrical load prediction.Firstly,the electric load eigen mode functions with different center frequencies and relatively stable ones are obtained by VMD.Then,after calculating the value of zero cross rate,the modal components of each group are superimposed respectively to form the high-frequency and low-frequency timing components,and the Prophet model is used to extract the high-frequency components for timing features.Finally,the ARIMA prediction model is used to predict the low frequency component,and the LSTM neural network model is applied to predict the high frequency component.The final predicted electric load is obtained by superimposing the respective prediction results.The proposed method is applied to the actual integrated energy system,and the example analysis shows that the combined forecasting method presented above has good forecasting performance for the integrated energy system.
integrated energy systemload forecastingvariational mode decompositionLSTM neural networkProphet model
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