Use of weighted local constant method to short-term forecasting of electric load in cities at weekends

Abstract

To forecast accurately the electric load of a city at the weekend, aiming at the problems of nonlinearity and model interpretability in power behavior, a local constant first-order weighted forecasting model is developed. First of all, the values of the electric load are reconstructed in phase space, stability analysis can effectively describe the operation mechanism and law of weekend power. Then, the weights of neighbors are used in the first-order local forecasting model, and the nearest neighbors are used in grid optimization. The forecasted values are then separated to be the phase forecasting points in terms of the spatial dimension of the electric loads and the lags of the time delay obtained in the training stage. Numerical experimental results of samples at different scales show the following: (1)The forecasting performance of the proposed model is better than other models in the forecasting errors in this work (among three types of measurements: root mean square error (RMSE), mean absolute percentage error (MAPE), and mean absolute error (MAE)); (2) the significance test (KSPA) also confirmed the universality and consistency of the method, and the effective diagnosis and statistical analysis of the weekend power mechanism are helpful to promote weekend power management and commercial design.