Multiple BP neural network load prediction method based on grey correlation degree

Abstract

The invention discloses a multiple BP neural network load prediction method based on grey correlation degree. The method comprises the following steps: 1) carrying out load sequence correlation analysis based on grey correlation degree; 2) carrying out clustering based on a shortest distance method to determine a member set of a multiple BP neural network; 3) determining multiple number of the multiple BP neural network based on effectiveness index; 4) improving the BP neural network by introducing a momentum factor and adopting a multiple-calculation averaging method to solve the problem of easy local convergence of the BP neural network, and improve anti-vibration capability thereof; and 5) carrying out short-term power load prediction through an established multiple BP neural network prediction model. The method solves the problem of easy local convergence of the BP neural network and improves anti-vibration capability thereof; and compared with a conventional BP neural network prediction model, the multiple BP neural network has a better prediction effect.

Description

technical field [0001] The invention relates to the technical field of application of short-term load forecasting in power systems, in particular to a load forecasting method based on multiple BP neural networks based on gray relational degrees. Background technique [0002] Power load forecasting is of great significance in ensuring power system planning, reliable and economical operation. With the continuous progress of modern technology and the deepening of smart grid, the theory and technology of load forecasting have been greatly developed. Over the years, power load forecasting methods and theories have emerged continuously. Technologies such as time series method, fuzzy theory, regression analysis method, regression support vector machine, Bayesian and neural network have provided good technical support for power load forecasting. However, the existing algorithms still have certain limitations. Time series method: the accuracy of historical data is high, and it is n...

AI Technical Summary

Problems solved by technology

[0004] The technical problem to be solved by the present invention is to provide a load forecasting method of multiple BP neural networks based on gray relational degree, aiming at the problem of weak generalization ability due to the existence of "overfitting" in traditional BP neural network applied to load forecasting , based on the gray relational degree and the shortest distance method, define the validity index that characterizes the advantages and disadvantages of clustering, so as to determine the reasonable multiplicity of the prediction model

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