Electric power system short period load prediction method based on combined neural network

Abstract

The invention belongs to the power system short-term load prediction technology field and discloses an electric power system short period load prediction method based on the combined neural network. The method comprises steps that (1), historical power grid load and meteorological data are collected, and a database is built for backup; (2) abnormal data acquired in the step (1) is removed, and normalization processing of the residual data is carried out; (3), a model structure having feedforward and feedback functions is determined; (4), the historical data is utilized to train a prediction model, and model parameters and weights are determined; and (5), the prediction model is utilized for actual load prediction to acquire a prediction load value. The method is advantaged in that the combined neural network is composed of an AM-NN sub model based on the additional momentum method and a QN-NN sub model based on the Quasi-Newton method, the two models are fused through a time-varying comprehensive weight coefficient, meteorological factor data is introduced to the model, the rolling optimization strategy is employed, the model is made to have relatively good generalization and convergence property, and actual scheduling prediction requirements can be more precisely satisfied.

Description

technical field [0001] The invention relates to the technical field of short-term load forecasting of electric power systems, in particular to a method for short-term load forecasting of electric power systems based on combined neural networks. Background technique [0002] As the scale and complexity of power systems continue to increase, the accuracy of short-term load forecasting in power systems plays a key role in effectively reducing power generation costs and implementing optimal control of power systems in various regions. Compared with long-term load forecasting, short-term load forecasting is mainly used to arrange power generation planning, and has the highest timeliness. Its load changes quickly and is greatly affected by abrupt factors such as temperature difference and humidity, and belongs to a dynamic nonlinear time series. Due to such characteristics of short-term load, it is difficult to achieve accurate forecasting. With the implementation of the new ele...

AI Technical Summary

Problems solved by technology

Due to such characteristics of short-term load, it is difficult to achieve accurate forecasting

Images