Photovoltaic power generation power prediction method based on similar day theory and PCA-PSO-BP

A PCA-PSO-BP, photovoltaic power generation technology, applied in forecasting, neural learning methods, data processing applications, etc., can solve problems such as long forecasting time, and achieve a reduction in the number of calculations, short power generation time, and high forecasting accuracy. Effect

Pending Publication Date: 2021-03-19
HUNAN UNIV OF TECH
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Problems solved by technology

[0004] The present invention aims at the problem that the prediction accuracy of photovoltaic power generation needs to be improved and the prediction time is too long in the prior art

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  • Photovoltaic power generation power prediction method based on similar day theory and PCA-PSO-BP
  • Photovoltaic power generation power prediction method based on similar day theory and PCA-PSO-BP
  • Photovoltaic power generation power prediction method based on similar day theory and PCA-PSO-BP

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Embodiment Construction

[0033] The present invention will be further described below in combination with specific embodiments.

[0034] A photovoltaic power prediction method based on similar day theory and PCA-PSO-BP:

[0035] S1. Establish a photovoltaic power generation prediction model based on PCA-PSO-BP;

[0036] S2. Based on the similar day theory, use the gray relational degree method to select the similar day of the forecast day;

[0037] Radiation information, temperature information and humidity information are selected as the factors affecting the output of the optical field. Since these information are all factors affecting the output of photovoltaic power generation, it can roughly determine the output of photovoltaic power generation throughout the day. Search the historical data of the same season and the same weather as the forecast day to find similar days.

[0038] Based on the above analysis, the meteorological feature vector is constructed, that is, Y i =[I hi , I li , I a...

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Abstract

The invention provides a photovoltaic power generation power prediction method based on a similar day theory and PCA-PSO-BP. The photovoltaic power generation power prediction method comprises the following steps: S1, establishing a photovoltaic power generation prediction model based on PCA-PSO-BP; S2, based on a similarity theory, selecting a similar day of the prediction day by utilizing a greycorrelation degree method; S3, performing dimension reduction on the historical data of the similar days by using PCA; S4, taking the similar day data after dimension reduction as a training set of aPCA-PSO-BP photovoltaic power generation prediction model so as to predict the photovoltaic power generation power of the prediction day; the invention has high prediction precision under different weather conditions, the time for predicting the power generation power according to the historical data of the similar days is shorter, and the number of calculation times required for obtaining the final power generation power is correspondingly reduced.

Description

technical field [0001] The present invention relates to the field of photovoltaic power generation, and more specifically, to a photovoltaic power prediction method based on similar day theory and PCA-PSO-BP. Background technique [0002] Photovoltaic power generation is the most widely used new energy source, and its output power is fluctuating and intermittent due to the influence of the weather environment. Therefore, the impact of photovoltaic power generation fluctuations on the power grid cannot be ignored. By accurately predicting the power of photovoltaic power generation, the impact of photovoltaic power generation uncertainty on the power grid can be reduced, the operating status of the power system can be improved, and the economic operation of the system can be maintained. [0003] In terms of photovoltaic power prediction, many domestic and foreign scholars have done a lot of research by using different theories and methods to establish models. Among them, fore...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06K9/62G06N3/08G06F17/16
CPCG06Q10/04G06Q50/06G06N3/084G06F17/16G06F18/213G06F18/214
Inventor 于惠钧李秉晨
Owner HUNAN UNIV OF TECH
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