Short-term power prediction method based on genetic algorithm to optimize Elman neural network

A neural network and genetic algorithm technology, applied in the field of photovoltaic power generation forecasting, can solve problems such as the dynamic characteristics of the problem that cannot be responded well, and achieve the effects of easy scheduling operation, high prediction accuracy and fast speed.

Inactive Publication Date: 2018-10-16
DONGHUA UNIV
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  • Short-term power prediction method based on genetic algorithm to optimize Elman neural network
  • Short-term power prediction method based on genetic algorithm to optimize Elman neural network
  • Short-term power prediction method based on genetic algorithm to optimize Elman neural network

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[0021] The present invention will be further explained below in conjunction with specific embodiments. It should be understood that these embodiments are only used to illustrate the present invention and not to limit the scope of the present invention. In addition, it should be understood that after reading the teachings of the present invention, those skilled in the art can make various changes or modifications to the present invention, and these equivalent forms also fall within the scope defined by the appended claims of this application.

[0022] The embodiment of the present invention relates to a short-term power prediction method for optimizing the Elman neural network based on a genetic algorithm. First, the Elman neural network topology is determined, including the number of input layer nodes, the number of hidden layer nodes, and the number of output layer nodes of the neural network. , Undertake the number of layer nodes, etc. Then initialize the weight threshold leng...

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Abstract

The invention relates to a short-term power prediction method based on a genetic algorithm to optimize an Elman neural network. The method comprises the following steps: firstly determining the Elmanneural network topology structure, wherein the Elman neural network topology structure comprises the number of nodes in the neural network input layer, the number of hidden layer nodes, the number ofoutput layer nodes, and the number of nodes in the receiving layer and the like. Then initializing the Elman neural network weight threshold length. Then using the genetic algorithm to encode the initial value and cross-variation to generate the initial weight of the optimized neural network. Finally, learning and training the neural network and updating the weight to obtain the prediction result.According to the Short-term power prediction method based on genetic algorithm to optimize Elman neural network, the prediction accuracy is higher, the speed is faster, and the dispatching operationof the power grid is convenient.

Description

technical field [0001] The invention relates to the technical field of photovoltaic power generation forecasting, in particular to a short-term power forecasting method based on genetic algorithm optimization of Elman neural network. Background technique [0002] In recent years, photovoltaic grid-connected power generation technology has become increasingly mature and widely used. Photovoltaic power system is composed of power grid and power users. Its task is to convert solar energy into electrical energy to provide users with economical, reliable, and quality-standard electrical energy without interruption. Meet the needs of various loads and provide power for social development. Due to the particularity of the production and use of photovoltaic power, that is, it is difficult to store a large amount of power, and the demand for power of various users is constantly changing. The system should maximize the capabilities of the equipment to keep the entire system running ...

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/08
CPCG06Q10/04G06N3/086G06Q50/06
Inventor 周武能尤亚锋
Owner DONGHUA UNIV
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