Photovoltaic power station short-term power prediction method based on recurrent neural network

A technology of cyclic neural network and photovoltaic power station, applied in the field of short-term power prediction of photovoltaic power station based on cyclic neural network, can solve the problems of large randomness, strong fluctuation of solar energy, unfavorable safe and stable operation of power grid, etc., to improve accuracy and reliability Effect

Pending Publication Date: 2020-02-07
FUZHOU UNIVERSITY
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[0002] In recent years, fossil fuels have been gradually depleted and environmental pollution has become increasingly serious, which has attracted widespread attention from all over the world. For the continuation of human civilization, taking the road of sustainable development is the only way in the future. At present, it is urgent to find new energy to bear the burden of human society The energy required for operati

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  • Photovoltaic power station short-term power prediction method based on recurrent neural network
  • Photovoltaic power station short-term power prediction method based on recurrent neural network
  • Photovoltaic power station short-term power prediction method based on recurrent neural network

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[0037] The present invention will be further described below in conjunction with the drawings and embodiments.

[0038] Please refer to figure 1 , The present invention provides a method for short-term power prediction of photovoltaic power plants based on cyclic neural network, including the following steps:

[0039] Step S1: Obtain corresponding NWP meteorological parameters according to the weather type of the day to be predicted;

[0040] Step S2: Collect historical data historical power and historical NWP meteorological parameters several days before the day to be predicted;

[0041] Step S3: Process historical power and historical NWP meteorological parameters, and use the processed historical data as a training data set;

[0042] Step S4: Use the cyclic neural network to learn the training data set, and use the stochastic gradient descent method to adjust the parameters of the network to obtain a prediction model;

[0043] Step S5: Use the NWP meteorological parameters of the day ...

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Abstract

The invention relates to a photovoltaic power station short-term power prediction method based on a recurrent neural network, and the method comprises the following steps: S1, obtaining a corresponding NWP meteorological parameter according to the weather type of a to-be-predicted day; S2, collecting historical data of a plurality of days before a to-be-predicted day; S3, processing the historicaldata, and taking the processed historical data as a training data set; S4, learning the training data set by using a recurrent neural network, and adjusting parameters of the network by using a stochastic gradient descent method to obtain a prediction model; and S5, taking the NWP meteorological parameters of the day to be predicted as the input of the prediction model to obtain a predicted powervalue. The method can remarkably improve the precision and reliability of short-term power prediction of the photovoltaic power station.

Description

technical field [0001] The invention belongs to short-term prediction technology of photovoltaic power station power, in particular to a short-term power prediction method of photovoltaic power station based on cyclic neural network. Background technique [0002] In recent years, fossil fuels have been gradually depleted and environmental pollution has become increasingly serious, which has attracted widespread attention from all over the world. For the continuation of human civilization, taking the road of sustainable development is the only way in the future. At present, it is urgent to find new energy to bear the burden of human society The energy required for operation, solar energy is the most attention-grabbing new energy, but due to the strong volatility and randomness of solar energy, this will not be conducive to the safe and stable operation of the power grid, so the photovoltaic power forecasting technology is used to advance the production capacity of photovoltaic...

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Application Information

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IPC IPC(8): G06N3/04G06N3/08G06Q10/04G06Q50/06
CPCG06N3/084G06Q10/04G06Q50/06G06N3/045
Inventor 陈志聪陈辉煌吴丽君程树英林培杰
Owner FUZHOU UNIVERSITY
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