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

longand 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.