A time series-based method for predicting electricity demand in industrial parks

In order to accurately predict electricity demand and improve the economy and security of the power system, a time series based method for predicting electricity demand in industrial parks is proposed. Firstly, the missing values of electricity consumption data are estimated using a seasonal exponential smoothing model. Then, the missing values are supplemented and the time series is decomposed. For each decomposed part, a suitable model is selected for fitting. For long-term trends, use univariate linear regression prediction method. For seasonal changes, choose seasonal ARIMA model for modelling. For periodic changes, use Fourier analysis method for prediction. For irregular changes, combine univariate linear regression prediction method and binary linear regression prediction method for prediction. Finally, the GARCH model is introduced to test the error sequence. The experimental results show that the proposed method improves the accuracy of the prediction model and has practical application value.