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Photovoltaic generated power prediction method based on multi-period comprehensive similar days

A technology of photovoltaic power generation and prediction method, which is applied in prediction, instrument, biological neural network model, etc., can solve problems such as insufficient correlation between temperature and photovoltaic output power data, and error in prediction results.

Inactive Publication Date: 2015-03-25
HOHAI UNIV
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Problems solved by technology

It’s just that the general selection of similar days is based on the weather forecast. Since the weather forecast only provides temperature data, and the correlation between temperature and photovoltaic output power data is not strong enough, there will be large errors in the prediction results.

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[0051] The technical solutions of the present invention will be described in detail below in conjunction with the accompanying drawings.

[0052] The present invention realizes photovoltaic output prediction based on BP neural network, and the BP neural network adopts figure 1 structure shown.

[0053] figure 1 In , the input variable is an m-dimensional vector, and the m-dimensional vector is defined as X=(x 1 ,x 2 ,...,x n ,...,x n+p ,...,x m ), where x 1 ,x 2 ,...,x n It is the power generation of n points corresponding to a similar day on the forecast day, n represents the number of meteorological feature vector components, x n+1 ,...,x n+p is the meteorological parameter corresponding to the previous similar day, x n+p+1 ,...,x m is the meteorological parameter corresponding to the forecast day, the output variable o 1 ,o 2 ,...,o n In order to predict the power generation of n points corresponding to different time segments in a day; the BP neural network ...

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Abstract

The invention discloses a photovoltaic generated power prediction method based on multi-period comprehensive similar days. The method comprises the steps that firstly, the Euclidean distance method is adopted for classifying weather types and segmenting prediction days; historical similar days are selected at different periods of time, and the BP neural network is adopted for predicting the generated power of the corresponding period of time; a day feature similar day function is applied for predicting the first period of time to obtain a prediction power value of the first period of time; the obtained power value of the first period of time is combined with a linear comprehensive similar day function to obtain the comprehensive similar days, and the comprehensive similar days are applied for predicting the second period of time to obtain a prediction power value of the second period of time; then, power values of the follow-up periods of time are predicted by repeatedly executing the method for the second period of time according to the prediction power values obtained by the previous period of time; the prediction results of the periods of time are combined, and therefore photovoltaic generated power output data of the whole day to be predicted are obtained. According to the prediction method, the photovoltaic generated power prediction accuracy can be effectively improved.

Description

technical field [0001] The invention belongs to the technical field of photovoltaic power generation, and in particular relates to a method for predicting photovoltaic power generation power based on similar days in a photovoltaic power generation system. Background technique [0002] In order to ensure the safe and stable operation of the power system, enable the power dispatching department to dispatch in time according to changes in photovoltaic power generation, and reduce reserve capacity and operating costs, it is necessary to accurately predict the power generation of photovoltaic power plants. [0003] At present, the research on the output power of photovoltaic power generation system at home and abroad is getting more and more in-depth. According to the difference of predicted physical quantities, it can be summarized into the following two categories: ① indirect prediction method based on solar radiation intensity, ② direct prediction method using historical output...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/02
CPCG06Q10/04G06N3/02G06Q50/06
Inventor 王冰卢舟鑫
Owner HOHAI UNIV
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