A power load prediction method based on a Bayesian regularization neural network

Abstract

The invention discloses a power load prediction method based on a Bayesian regularization neural network, and the method comprises the following steps: obtaining electric quantity historical data, andanalyzing a key factor influencing the increase of electric quantity; determining a BP neural network structure; Training the network by using a BP algorithm to minimize the total error F (W); calculating the number of effective parameters; calculating a new estimated value of the hyper-parameter sum by using a Bayesian method; Repeatedly executing the above steps until the required precision isachieved, thereby completing the establishment of the Bayesian regularization optimization neural network; and inputting a new key factor influencing the increase of the power consumption to obtain the whole-society power load condition of the time period. The method has the advantages that the Bayesian method is applied to the modeling process of the neural network, the regularization method is used for correcting the training performance function of the neural network to improve the generalization ability of the neural network, the convergence speed is high, and a smaller training error canbe obtained.

Description

technical field [0001] The invention relates to a power load forecasting method based on a Bayesian regularization neural network. Background technique [0002] The power system is composed of the power grid and power users. Its task is to continuously provide economical, reliable and quality-standard electric energy to the majority of users, meet the needs of various loads, and provide power for economic and social development. Due to the particularity of the production and use of electric power, that is, it is difficult to store a large amount of electric energy, and the demand for electric power of various users is constantly changing, which requires that the power generation of the system should be dynamically balanced with changes in the system load at any time, that is, the system should maximize Give full play to the capabilities of the equipment to keep the entire system running stably and efficiently to meet the needs of users. Otherwise, it will affect the quality...

AI Technical Summary

Problems solved by technology

[0003] The purpose of the present invention is to provide a power load forecasting method based on Bayesian regularized neural network, which can effectively solve the problems of local convergence, slow convergence speed and overfitting of existing forecasting methods

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