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Feature clustering comparison-based power prediction method and device for photovoltaic power station

A power prediction and photovoltaic power station technology, applied in neural learning methods, instruments, biological neural network models, etc., can solve the problems of low accuracy and low accuracy of photovoltaic power station power prediction, and achieve more targeted training and improved prediction accuracy. Effect

Inactive Publication Date: 2017-05-10
XUJI GRP +1
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Problems solved by technology

[0005] The purpose of the present invention is to provide a method for predicting the power of photovoltaic power plants based on feature clustering and comparison, and solve the problems of low power prediction accuracy and low accuracy of huge amounts of current photovoltaic power plants

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Embodiment Construction

[0029] The specific embodiments of the present invention will be further described below in conjunction with the accompanying drawings.

[0030] Embodiment of the method for predicting the power of photovoltaic power plants based on feature clustering comparison in the present invention

[0031] The photovoltaic power plant power prediction method of the present invention firstly obtains the characteristic quantity that affects the photovoltaic power prediction accuracy, and uses the historical data of the characteristic quantity to form a sample set; then uses the feature clustering algorithm to gather the samples into k categories, and uses various historical data to establish the corresponding category. Prediction model; finally calculate the distance between the current object and various cluster centers, and select the prediction model corresponding to the class of the cluster center closest to the current object to predict the current object, so as to realize the predicti...

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Abstract

The present invention relates to a feature clustering comparison-based power prediction method and a device for a photovoltaic power station and belongs to the technical field of photovoltaic power generation. According to the technical scheme of the method, firstly, three most significant feature amounts that influence the prediction precision of the photovoltaic power are acquired and accumulated as historical meteorological data. Secondly, the obtained historical meteorological data are adopted as data samples to be clustered, so that the samples are divided into k types of high similarity. Meanwhile, the cluster center of each type of samples is obtained. Thirdly, a prediction model of a corresponding type is established based on the historical data of each type. Fourthly, a corresponding prediction model nearest to the cluster center of a current object is selected for prediction. In this way, different meteorological data are divided into different types of samples. Meanwhile, photovoltaic output prediction models in different meteorological conditions are established, so that the prediction models are trained in the more targeted manner. Moreover, the power prediction is conducted based on the power prediction models established in different meteorological conditions. Therefore, the prediction accuracy of the photovoltaic power is improved.

Description

technical field [0001] The invention relates to a method and device for predicting the power of a photovoltaic power station based on feature clustering comparison, and belongs to the technical field of photovoltaic power generation. Background technique [0002] As a kind of clean energy, photovoltaic power generation has received extensive attention worldwide. There are more and more large-scale ground photovoltaic power stations and distributed rooftop photovoltaic power stations in my country. However, due to the volatility and intermittent nature of photovoltaic power generation, power grid Scheduling has brought great difficulties, and the development of photovoltaic power forecasting has effectively alleviated this problem. However, the photovoltaic power forecasting errors currently on the market are relatively large, and it is difficult to provide effective reference for grid dispatching. [0003] At present, my country has carried out some technical research on the ...

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Application Information

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IPC IPC(8): G06K9/62G06N3/08G06Q50/06
CPCG06N3/084G06Q50/06G06F18/23213
Inventor 董永超李宝峰焦东东陈娜娜葛琪谢红伟霍富强王留送李现伟
Owner XUJI GRP
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