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.

Improving the accuracy of financing risk prediction is of great significance to the healthy development of grid enterprises. Taking a provincial-level power grid company as the research object, the financing risk index system is constructed by considering multiple dimensions, and the monthly financing risk index RI of power grid enterprises from 2015-2018 is determined based on entropy weight and comprehensive index method, while the financing risk prediction model is constructed with the help of extreme gradient boosting tree model. The empirical results show that compared with support vector regression and BP neural network models, the financing risk prediction model constructed based on the extreme gradient boosting model has an excellent performance in terms of prediction accuracy and stability.

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