Short-term electricity consumption data reflects the operating efficiency of grid companies, and accurate forecasting of electricity consumption helps to achieve refined electricity consumption planning and improve transmission and distribution transportation efficiency. In view of the fact that the power consumption data is nonstationary, nonlinear, and greatly influenced by the season, holidays, and other factors, this paper adopts a time-series prediction model based on the EMD-Fbprophet-LSTM method to make short-term power consumption prediction for an enterprise's daily power consumption data. The EMD model was used to decompose the time series into a multisong intrinsic mode function (IMF) and a residual component, and then the Fbprophet method was used to predict the IMF component. The LSTM model is used to predict the short-term electricity consumption, and finally the prediction value of the combined model is measured based on the weights of the single Fbprophet and LSTM models. Compared with the single time-series prediction model, the time-series prediction model based on the EMD-Fbprophet-LSTM method has higher prediction accuracy and can effectively improve the accuracy of short-term regional electricity consumption prediction.

No abstract

The sales of electricity have great effect on operation decisions and budget arrangement of power grid companies directly, so the accuracy of electricity sale forecasting is quite crucial. Since the sales of electricity data has obvious increase trend with time and seasonal variation characteristics, single ARIMA model cannot get ideal forecasting results. According to the monthly electricity sales data of Z Province from 2011 January to 2014 October, this paper builds SARIMA model and get high forecast precision. In addition, since the sales of electricity is affected by temperature obviously and there are many temperature anomaly days in Z Province in 2014 compared with previous years, this paper processed the temperature correction and further improved the prediction accuracy.