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Photovoltaic power generation power short-term prediction method based on artificial bee colony optimization neural network

A photovoltaic power generation and artificial bee colony optimization technology, which is applied in neural learning methods, biological neural network models, predictions, etc., can solve problems such as difficult real-time acquisition and low accuracy

Pending Publication Date: 2020-10-30
DATANG DONGBEI ELECTRIC POWER TESTING & RES INST
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Problems solved by technology

[0004] In view of the low accuracy of the existing atmospheric aerosol optical depth (AOD) retrieval method and the difficulty of real-time acquisition, it is necessary to propose a new short-term prediction method for photovoltaic power generation to better predict photovoltaic power generation

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  • Photovoltaic power generation power short-term prediction method based on artificial bee colony optimization neural network
  • Photovoltaic power generation power short-term prediction method based on artificial bee colony optimization neural network
  • Photovoltaic power generation power short-term prediction method based on artificial bee colony optimization neural network

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[0068] The specific implementation manners of the present invention will be further described in detail below in conjunction with the accompanying drawings and embodiments. The following examples are used to illustrate the present invention, but are not intended to limit the scope of the present invention.

[0069] ginseng figure 1 As shown, this embodiment provides a short-term prediction method for photovoltaic power generation based on artificial bee colony optimization neural network, including the following steps:

[0070] Step S1, establish and train the BP neural network prediction model based on the correlation of photovoltaic power influencing factors, and optimize the BP neural network prediction model based on the artificial bee colony algorithm to obtain the BP neural network prediction model after artificial bee colony optimization;

[0071] Step S2, based on the BP neural network prediction model optimized by the artificial bee colony, the atmospheric aerosol op...

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Abstract

The invention relates to a photovoltaic power generation power short-term prediction method based on an artificial bee colony optimization neural network. The method comprises the following steps: step 1, establishing and training a BP neural network prediction model based on photovoltaic power generation power influence factor discrimination correlation, and optimizing the BP neural network prediction model based on an artificial bee colony algorithm to obtain an artificial bee colony optimized BP neural network prediction model; step 2, based on the BP neural network prediction model after artificial bee colony optimization, taking pM2.5 concentration, PM10 concentration, visibility, humidity, wind speed, atmospheric aerosol optical thickness AOD440 waveband and AOD1020 waveband as input variables, and estimating an AOD440 waveband and an AOD1020 waveband of the optical thickness of the atmospheric aerosol to obtain predicted values of the AOD440 waveband and the AOD1020 waveband; and 3, calculating the solar irradiance based on the predicted values of the AOD440 and AOD1020 wavebands, and calculating the photovoltaic power generation power through photoelectric conversion. According to the invention, the short-term power prediction of photovoltaic power generation can be realized, and the accuracy is high.

Description

technical field [0001] The invention relates to the technical field of photovoltaic power generation, in particular to a short-term prediction method of photovoltaic power generation based on artificial bee colony optimization neural network. Background technique [0002] Photovoltaic power generation is a key way to effectively utilize solar energy. Large-scale grid-connected photovoltaic power generation is an important development direction of the future power generation industry. At present, large-scale grid-connected photovoltaic power generation has been promoted. Photovoltaic power generation is affected by various meteorological environments, so it will have an impact and impact on power grid dispatching. [0003] Irradiance is the most critical factor in the process of photovoltaic output. It is greatly attenuated by aerosol particles before acting on photovoltaic cells, and the aerosol optical depth (AOD) data is mainly obtained through Moderate-resolution Imaging...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q50/06G06Q10/04G06N3/00G06N3/08
CPCG06Q50/06G06Q10/04G06N3/006G06N3/084Y04S10/50
Inventor 李金拓袁智马博洋董蔚李强杜晶
Owner DATANG DONGBEI ELECTRIC POWER TESTING & RES INST
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