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Intelligent power grid short-term load predication method based on improved RBF neural network

A short-term load forecasting, smart grid technology, applied in neural learning methods, biological neural network models, forecasting, etc., can solve the problems of low accuracy, complex methods, and many dimensions of smart grid load forecasting

Active Publication Date: 2017-04-26
BEIJING UNIV OF POSTS & TELECOMM +1
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  • Application Information

AI Technical Summary

Problems solved by technology

[0004] In addition, for the RBF neural network basis function center, the FCM method is mainly used to determine, but in the smart grid load forecasting, the FCM method load data scale is large, the dimension is large, and the method is more complicated, which ultimately makes the load forecasting accuracy of the smart grid lower.

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  • Intelligent power grid short-term load predication method based on improved RBF neural network
  • Intelligent power grid short-term load predication method based on improved RBF neural network
  • Intelligent power grid short-term load predication method based on improved RBF neural network

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Embodiment 1

[0043] This embodiment proposes a smart grid short-term load forecasting method based on an improved RBF neural network. This method uses the PCA-WFCM clustering algorithm to determine the RBF basis function center c i , using the gradient descent method to determine the connection weights between the hidden layer and the output layer of the RBF neural network. The following is a detailed description of the smart grid short-term load forecasting method based on the improved RBF neural network:

[0044] First establish sample data X={x 1 ,x 2 ,...,x N},x j For each sample point, x j ={x j1 ,x j2 ,...,x js}, j=1,2,...N, there are N sample points in total, x j Indicates that each sample point contains s attributes. In cluster analysis, the sample data X is divided into K categories, and the range of K is 2≤K≤N; the cluster center V={v 1 ,v 2 ,...,v k}, a total of K cluster centers.

[0045] Radial basis function (RBF) neural network is a feed-forward neural network b...

Embodiment 2

[0079] According to the idea in the first embodiment, the neural network prediction in this embodiment needs to determine the input and output of the neural network and the number of hidden layer nodes in advance. The network input is determined by a series of parameters that affect the predicted value. Because the smart grid user load curve has good periodic characteristics, the influence of the load value at a certain moment can consider the daily cycle characteristics and weekly cycle characteristics, that is, the load value at the same time of the day before the forecast time and the load value at the same time of the previous week can be selected . Specifically predict the number of neurons in the input layer N Ⅰ =9, including 8 load points: load value L(t-1) at the moment before the forecast point, load value L(t-2) at two moments before the forecast point, load value L(t-48) at the same forecast point the day before , Load value L at the moment before the same predict...

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Abstract

The invention discloses an intelligent power grid short-term load prediction method based on an improved RBF neural network, relates to the technical field of intelligent power grid, and is used for determining the basis function center and improving the load prediction precision of the intelligent power grid. The prediction method includes: S1, performing network initialization; S2, calculating the basis function center ci; S3, calculating the variance [zeta]i according to the basis function center ci; S4, calculating the output Ri of a hidden layer according to the basis function center ci and the variance [zeta]i; S5, calculating the output of an output layer according to the output Ri of the hidden layer; S6, calculating a prediction error E according to a mean squared error and the function; S7, updating connecting weights of neurons of the hidden layer and neurons of the output layer in the neural network; and S8, determining the prediction error E, if the prediction error E is expected, ending iterative calculation, and otherwise, returning to step S4, and re-performing iterative calculation on the prediction error E. The method is used for predicting the load of the power grid.

Description

technical field [0001] The invention relates to the technical field of smart grids, in particular to a short-term load forecasting method for smart grids based on an improved RBF neural network. Background technique [0002] The rapid development of the smart grid has produced a large amount of electricity consumption data (also called sample data), and it is of great significance to analyze these sample data. Using the forecasting method to apply sample data to short-term load forecasting can improve the accuracy of load forecasting, which plays an important role in the safe scheduling and economical operation of the power system. Radial Basis Function (RBF) neural network is the most widely used forecasting method in load forecasting, because it is a local approximation network that can approximate any continuous function with arbitrary precision and has the only optimal Approximate characteristics and no local minimum problem, and the topology is simple and the learning ...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/04G06N3/08
CPCG06N3/08G06Q10/04G06Q50/06G06N3/048
Inventor 张天魁鲁云肖霖杨鼎成
Owner BEIJING UNIV OF POSTS & TELECOMM
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