Photovoltaic power station power prediction method and system based on recurrent neural network

A technology of cyclic neural network and photovoltaic power station, applied in the field of photovoltaic power generation, can solve the problems of accuracy attenuation, effective duration to be improved, and limited extrapolation ability, and achieve the effects of improving prediction accuracy, sufficient learning, and accurate prediction

Pending Publication Date: 2022-01-11
北京航天创智科技有限公司
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However, the extrapolation ability of this kind of method is limited. Usually, it can only have a high accuracy within 1 to 4 hours, and the accu

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  • Photovoltaic power station power prediction method and system based on recurrent neural network
  • Photovoltaic power station power prediction method and system based on recurrent neural network

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[0067] In order to make the object, technical solution and advantages of the present invention clearer, the present invention will be further described in detail below in combination with specific embodiments and with reference to the accompanying drawings. It should be understood that these descriptions are exemplary only, and are not intended to limit the scope of the present invention. Also, in the following description, descriptions of well-known structures and techniques are omitted to avoid unnecessarily obscuring the concept of the present invention.

[0068] Provide a method for predicting the power of photovoltaic power plants based on cyclic neural network, combined with Figure 1-2 , including the following steps:

[0069] (1) Obtain the historical output power data and weather forecast data recorded by the photovoltaic power plant.

[0070] In one embodiment, the historical output power (MW) recorded by the photovoltaic power plant with a time interval of 15 minu...

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Abstract

The invention relates to a photovoltaic power station power prediction method and system based on a recurrent neural network. The method comprises steps of acquiring historical output power data and weather forecast data recorded by a photovoltaic power station; performing data processing to obtain a historical output power data time sequence and a corresponding historical meteorological data time sequence, and performing normalization processing and segmentation to form a sample data set; and constructing and training a recurrent neural network model. Collecting output power data in a period of time, performing data processing, and inputting the data into the recurrent neural network model; and outputting a prediction result by the recurrent neural network model, and obtaining corresponding output power data as a photovoltaic power station power prediction value. According to the method, the photovoltaic power prediction model based on the recurrent neural network model is trained by combining the historical data of the photovoltaic power station and the NWP weather forecast data, the photovoltaic power generation power in the next 24 hours is predicted, and the prediction precision is improved.

Description

technical field [0001] The present invention relates to the technical field of photovoltaic power generation, in particular to a method and system for predicting the power of a photovoltaic power station based on a cyclic neural network. Background technique [0002] In recent years, with the rapid economic growth, more and more environmental problems have emerged, and the carbon dioxide greenhouse gas has soared, posing a threat to the living system. In this context, countries around the world have adopted a global agreement to reduce greenhouse gas emissions. In order to achieve this great goal, all regions should actively adjust the energy structure, optimize the industrial layout, develop new energy industries, and adhere to green and low-carbon development. As the focus of carbon emissions, the power industry must vigorously develop photovoltaic and other new energy power generation industries and continuously optimize the power structure. The so-called photovoltaic po...

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Application Information

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IPC IPC(8): G06F30/27G06Q10/04G06Q50/06G06N3/04G06N3/08G06F119/06
CPCG06F30/27G06Q10/04G06Q50/06G06N3/08G06F2119/06G06N3/044
Inventor 徐崇斌王鑫磊陈前左欣吴俣孙晓敏杨勇刘亮
Owner 北京航天创智科技有限公司
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