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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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But none of these can better reflect the dynamic characteristics of practical problems.

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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] Below in conjunction with specific embodiment, further illustrate the present invention. It should be understood that these examples are only used to illustrate the present invention and are not intended 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 the present application.

[0022] Embodiments of the present invention relate to a short-term power prediction method based on a genetic algorithm to optimize the Elman neural network. First, the topology of the Elman neural network is determined, including the number of neural network input layer nodes, the number of hidden layer nodes, and the number of output layer nodes. , the number of receiving layer nodes, etc. Then initialize the weight threshol...

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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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Application Information

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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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