Photoelectric probability density prediction method based on B-spline quantile regression

Abstract

The invention discloses a photoelectric probability density prediction method based on B-spline quantile regression, and the method comprises the steps: 1, collecting photoelectric data, carrying outthe normalization of the photoelectric data, and dividing historical photoelectric data into a training set and a test set; 2, constructing a B-spline quantile model, and calculating parameters of theB-spline quantile regression model by utilizing the training set data; and 3, substituting the test set data into the B spline quantile model to obtain predicted values under different quantiles, andrealizing photoelectric probability density prediction by applying kernel density estimation. The prediction precision of photovoltaic power generation can be improved, and the uncertainty of a prediction result is comprehensively measured, so that a reliable basis is provided for safely and stably connecting the photovoltaic power generation into a power grid.

Description

technical field [0001] The invention relates to the technical field of photoelectric power, and mainly relates to a photoelectric probability density prediction method based on B-spline quantile regression. Background technique [0002] Due to the increasingly serious environmental pollution and energy shortage, the application of renewable clean energy has been widely concerned. The global energy system is constantly changing. Compared with traditional petroleum and coal resources that cause air pollution and are non-renewable energy sources, renewable energy has become the preferred technology in the power market, and photovoltaic power generation is one of the important renewable energy power generation methods. Photovoltaic power generation uses solar energy resources to generate electricity. Its advantages are abundant solar energy resources, less impact of solar energy on regions, cleanliness and safety. However, photovoltaic power generation also has disadvantages su...

AI Technical Summary

Problems solved by technology

At present, many photovoltaic power generation researchers have studied photovoltaic power generation prediction methods and continuously improved the prediction accuracy of photovoltaic power generation. However, there is still a lot of room for improvement in the research on improving the prediction accuracy of photovoltaic power generation.

[0004] In addition, most of the traditional photovoltaic power generation prediction methods can only give point prediction results or interval prediction results of photovoltaic power generation, and cannot measure the uncertainty of photovoltaic power generation well.

And the prediction of photovoltaic power generation is usually affected by factors such as weather, and no power is generated at night. Therefore, the reliability of the obtained point prediction results and interval prediction results is low, and there is still room for research on photovoltaic power generation prediction methods

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