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

A technology of electric load and neural network, which is applied in the field of electric load forecasting, can solve problems such as overfitting and slow convergence speed, and achieve the effects of fast convergence speed, small training error, and improved generalization ability

Pending Publication Date: 2019-03-01
国网浙江瑞安市供电有限责任公司第二名称：瑞安市供电局 +3
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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

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

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Embodiment Construction

[0035] Embodiments of the invention are described in detail below, examples of which are illustrated in the accompanying drawings. The embodiments described below by referring to the figures are exemplary and are intended to explain the present invention and should not be construed as limiting the present invention.

[0036] A kind of electric load forecasting method based on Bayesian regularization neural network, comprises the following steps:

[0037] (1) Obtain the historical data of electricity consumption and analyze the key factors affecting the growth of electricity consumption;

[0038] (2) Determine the BP neural network structure, assign initial values ​​to the network parameters according to the prior distribution, and initialize the hyperparameters α and β; remember the neural network training model training sample D=(x i ,t i ), i=1,2,L,n, n is the total number of training samples, W is the network parameter vector, given the network structure H and network par...

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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...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/04
CPCG06Q10/04G06Q50/06G06N3/045
Inventor 叶铁丰郑明陈伟潘锡杰戴志博龙翔曹天亮杨凡
Owner 国网浙江瑞安市供电有限责任公司第二名称：瑞安市供电局
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