Neural network photovoltaic power generation output prediction method based on grey correlation analysis

A technology of grey relational analysis and output forecasting, applied in forecasting, instrumentation, data processing applications, etc., can solve problems such as slow convergence speed, low learning efficiency of BP neural network, and easy to fall into local optimum.

Inactive Publication Date: 2015-10-14
SOUTHEAST UNIV
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Problems solved by technology

[0004] However, the BP neural network has disadvantages such as low learning efficiency, slow convergence speed, and easy to fall into local optimum, which is not conducive to the improvement of predicti

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  • Neural network photovoltaic power generation output prediction method based on grey correlation analysis
  • Neural network photovoltaic power generation output prediction method based on grey correlation analysis
  • Neural network photovoltaic power generation output prediction method based on grey correlation analysis

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[0049] The present invention will be further described in detail below in conjunction with the accompanying drawings.

[0050] like figure 1 As shown, a genetic algorithm based on grey relational analysis to optimize BP neural network short-term output forecasting method of photovoltaic power generation includes the following steps:

[0051] A. Gray correlation analysis to determine the optimal training samples of similar small periods: the weather parameter information in each small period is formed into a behavior sequence, and the behavior sequence of the small period to be predicted and the selected sample small period are calculated by the method of gray correlation analysis. The comprehensive correlation coefficient of the behavior sequence; on this basis, the correlation degree analysis is carried out between one or more hours before the forecasted hour and one or more hours before the sample hour, and the fitting degree of the weather trend is obtained; The final corr...

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Abstract

The present invention relates to a neural network photovoltaic power generation output prediction method based on grey correlation analysis, which comprises grey correlation degree analysis, neural network training and output result prediction analysis. In the grey correlation degree analysis, an optimal sample is obtained by computing and sorting grey correlation degrees of hour period samples comprising factors which influence the photovoltaic output; according to the neural network training, the optical sample is utilized to train a BP neural network which is optimized by a genetic algorithm, so that the trained neural network is obtained; and according to the output result prediction analysis, weather parameter information in each hour period on prediction day is used as an input condition and is combined with the trained neural network to carry out prediction on an output in each time period by using hour as a step length and a mean absolute percentage error is adopted to carry out evaluation on prediction capacity of a system. According to the prediction method disclosed by the present invention, not only is prediction accuracy when weather is suddenly changed improved, but also the defect, that a prediction result is easy to fall into the local optimization, is avoided.

Description

technical field [0001] The invention relates to the field of photovoltaic power generation, in particular to a neural network photovoltaic power generation output prediction method based on gray relational analysis. Background technique [0002] With the deepening of the global energy crisis and the increasingly prominent climate issues, the development and utilization of renewable energy has received extensive attention. Solar photovoltaic power generation has become one of the most popular green energy sources due to its clean, safe and non-polluting features. However, solar photovoltaic power generation has the characteristics of volatility and intermittency, and large-scale photovoltaic grid connection will bring security, stability, power quality, reliability and other issues to the power grid. Therefore, the output prediction of photovoltaic power generation is of great significance to power grid planning and operation. [0003] At present, the models for photovoltaic...

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06
Inventor 陈中宗鹏鹏
Owner SOUTHEAST UNIV
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