Genetic support vector machine photovoltaic power interval prediction method based on quantile regression

Abstract

The invention discloses a genetic support vector machine photovoltaic power interval prediction method based on quantile regression. The method comprises the steps of extracting a solar radiation value, a temperature value and photovoltaic power of historical data to obtain a data sample, and performing normalization preprocessing; optimizing parameters of a support vector machine through a genetic algorithm to overcome the fluctuation and randomness of photovoltaic power generation, building a prediction model, and obtaining high-precision photovoltaic deterministic predictive power; and by analyzing a photovoltaic power prediction error of the prediction model, determining a quantile regression variable, and building a corresponding quantile regression model according to uncertain weather factors, so that photovoltaic power interval prediction is achieved. According to the method, photovoltaic power prediction error distribution does not need to be assumed; accurate photovoltaic power interval prediction ranges under different confidence degrees are obtained; richer information is provided for dispatching decision and operation risk assessment of an electric power system; and thetechnical problem of a non-ideal photovoltaic power interval prediction result is solved.

Description

technical field [0001] The invention belongs to the technical field of photovoltaic power generation prediction, and in particular relates to a quantile regression-based genetic support vector machine photovoltaic power interval prediction method. Background technique [0002] As the global fossil energy shortage and environmental pollution become increasingly serious, photovoltaics, as a renewable energy source, have rapidly increased their grid-connected capacity. Photovoltaic power generation has the advantages of simple structure, cleanness, safety, no noise, and high reliability. However, since photovoltaic power generation is affected by solar radiation intensity, battery components, temperature, weather clouds and some random factors, the system operation process is an unbalanced random process, and its power generation and output power fluctuate greatly and are uncontrollable. The performance is particularly prominent. After this power generation method is connected...

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Problems solved by technology

[0006] In order to solve the above problems, the present invention proposes a genetic support vector machine photovoltaic power interval prediction method based on quantile regression, which realizes the im

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