Short-term load predicting method for Elman neural network based on improved ABC algorithm

A short-term load forecasting and neural network technology, applied in the field of electric power, can solve problems such as low reliability of load forecasting results, neglect of seasonal weather diversity, and difficulty in covering weather with normal data, so as not to fall into local optimum and converge The effect of speed and stability improvement

Inactive Publication Date: 2018-11-02
JIANGSU UNIV
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Problems solved by technology

However, this method ignores the diversity of seasonal weather, and the stored normal data c

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  • Short-term load predicting method for Elman neural network based on improved ABC algorithm
  • Short-term load predicting method for Elman neural network based on improved ABC algorithm
  • Short-term load predicting method for Elman neural network based on improved ABC algorithm

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[0052] The present invention will be further described in detail below in conjunction with the accompanying drawings and specific embodiments.

[0053] The main contents of the present invention are: one, fully analyze the forward transmission of the input signal of the traditional Elman neural network, the backpropagation of the error signal and the delay operator process of the receiving layer; two: aiming at the artificial bee colony (ABC) algorithm convergence A series of improvement measures have been taken for the shortcomings of slow speed and weak development ability of search equations, including redesigning search equations, adjusting the search frequency of bees, and changing the selection mechanism of better solutions; 3. Applying the improved ABC algorithm to In the Elman neural network, the load forecasting function is realized in MATLAB.

[0054] 1. Analysis of traditional Elman neural network prediction principles

[0055] 1) Basic model of Elman neural networ...

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Abstract

The invention discloses a short-term load predicting method for an Elman neural network based on an improved ABC algorithm. The short-term load predicting method comprises the following steps: takinga series of improving measures specific to defects such as low converging speed of an artificial bee colony (ABC) algorithm and poor developing performance of a searching equation after forward transmission of an input signal of the conventional Elman neural network, backward transmission of an error signal and a delay operator of a carrying layer are fully analyzed, wherein the improving measuresinclude re-designing a searching equation, adjusting the honey searching frequency and changing the selection mechanism of an optimal solution and the like; applying an optimal weight generated by the improved ABC algorithm and a threshold value to the Elman neural network to realize short-term load prediction on a power system, and increasing the load prediction speed; and lastly, implementing aload prediction function in MATLAB, and optimizing the weight and the threshold value by adopting the improved ABC algorithm according to an experiment result, so that the maximum prediction absoluteerror is lowered remarkably.

Description

technical field [0001] The electric power technical field of the present invention is specifically a kind of Elman neural network short-term load forecasting method based on the improved ABC algorithm. Background technique [0002] Load forecasting in low-voltage station areas is one of the emerging tasks of the power supply department. Through accurate load forecasting, it is possible to economically and rationally adjust the operation mode, reduce the reserve capacity of the superior power station, rationally arrange maintenance plans, reduce operating costs, and improve economic benefits. According to the load forecasting theory of electric power system, in the process of forming the electric power trading market, the research of load forecasting is more important, and the load forecasting of low-voltage power supply station areas is also the same. Relatively speaking, the value of load forecasting in short and medium areas is more important to the management of low volta...

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Application Information

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IPC IPC(8): G06Q10/02G06N3/04G06Q50/06
CPCG06Q10/02G06Q50/06G06N3/044
Inventor 汪洋陈凤云王满商李正明闫天一潘天红
Owner JIANGSU UNIV
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