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Spiking neural network based photovoltaic system generation power prediction method

A technology for power generation and photovoltaic systems, applied in the field of power systems, can solve problems such as low accuracy of prediction results, and achieve the effects of improving accuracy and generalization capabilities, good approximation capabilities, and powerful computing capabilities

Active Publication Date: 2015-05-13
HOHAI UNIV
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Problems solved by technology

[0004] Purpose of the invention: In view of the problems existing in the prior art, the present invention aims at the defect that the accuracy of the prediction results is not high when the weather type, solar radiation intensity, ambient temperature and other influencing factors are considered in the existing photovoltaic system power prediction technology, and provides A prediction method of photovoltaic system power generation based on Spiking neural network

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  • Spiking neural network based photovoltaic system generation power prediction method
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  • Spiking neural network based photovoltaic system generation power prediction method

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[0023] Below in conjunction with specific embodiment, further illustrate the present invention, should be understood that these embodiments are only used to illustrate the present invention and are not intended to limit the scope of the present invention, after having read the present invention, those skilled in the art will understand various equivalent forms of the present invention All modifications fall within the scope defined by the appended claims of the present application.

[0024] The idea of ​​the present invention is to introduce the Spiking neural network into the power generation prediction of the photovoltaic system, and use the good nonlinear function approximation ability and powerful calculation ability of the Spiking neural network to improve the prediction accuracy of the power generation of the photovoltaic system.

[0025] Spiking neural network (Spiking Neural Network, SNN) is a neural network based on Spiking neurons. The Spiking neuron model adopts the...

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Abstract

The invention discloses a Spiking neural network based photovoltaic system generation power prediction method. The Spiking neural network based photovoltaic system generation power prediction method comprises the following steps of 1 calculating and sorting an Euclidean distance to determine similar day sets of prediction days of various weather types, 2 adopting Time-to-first-Spike method to convert analog quantity data into corresponding pulse time data, 3 utilizing a Spiking Neural Network (SNN) to perform modeling prediction on a converted time sample. By means of the Spiking neural network based photovoltaic system generation power prediction method, prediction accuracy is effectively improved, and the generation power prediction problem of a photovoltaic system can be well solved.

Description

technical field [0001] The invention relates to a method for predicting the generating power of a photovoltaic system, which is used to predict the generating power of a photovoltaic system, and belongs to the technical field of electric power systems. Background technique [0002] Actively promoting the application of photovoltaic power generation systems will help alleviate energy and environmental problems such as the depletion of fossil energy, the greenhouse effect, and air pollution. However, the power generation of photovoltaic systems is affected by factors such as solar radiation intensity and ambient temperature, and its power changes are random and fluctuating, which poses new challenges to the output arrangement of the power grid dispatching department. Therefore, it is necessary to accurately predict the power generation of the photovoltaic system and obtain its power generation curve in advance, which will help the grid dispatching department to make overall ar...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/08
CPCG06N3/084G06Q10/04G06Q50/06Y04S10/50
Inventor 孙国强陈通卫志农孙永辉范磊陈悦厉超
Owner HOHAI UNIV
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