Photovoltaic power prediction method based on seasonal regionalization

Abstract

The invention provides a photovoltaic power prediction method based on seasonal regionalization. The photovoltaic power prediction method comprises the steps: S1: collecting the historical information of a photoelectric power station; S2: classifying the solar irradiance data and the power data collected in the step S1 according to seasons; S3: preprocessing the data of every season after classification in the step S2, and using the preprocessed data to establish a support vector regression model to obtain the corresponding relationship between the power and the solar irradiance; S4: according to the season type belonging to a predicted day, determining a regression model which is demanded by power prediction; S5: aiming at the data of the time quantum before the predicted day, performing data preprocessing, and establishing a least squares model to obtain the relationship between the power and the solar irradiance within the recent time quantum; and S6: calculating the prediction power y. The model established by means of the photovoltaic power prediction method based on seasonal regionalization is high in versatility and generalization, and can improve the prediction accuracy of a photovoltaic power station to a certain degree.

Description

technical field [0001] The invention belongs to the field of power forecasting, in particular to a photovoltaic power forecasting method based on seasonal partitions. Background technique [0002] The existing power prediction model is to establish the corresponding relationship between power, solar irradiance and temperature based on the historical effective data of the power station, and then based on this corresponding relationship and the numerical weather forecast information in the future period, the predictive power. From the photovoltaic power generation mechanism and the composition structure of the photovoltaic power generation system, it can be seen that the output power of photovoltaic power plants will be affected by factors such as solar irradiance and temperature, and the law of the earth's revolution will lead to solar irradiance and temperature in China. Most regions show seasonality. If the seasonal factors are not considered, the universality and generali...

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Problems solved by technology

From the photovoltaic power generation mechanism and the composition structure of the photovoltaic power generation system, it can be seen that the output power of photovoltaic power plants will be affected by factors such as solar irradiance and temperature, and the law of the earth's revolution will lead to solar irradiance and temperature in China. Most of the regions show seasonal patterns. If the seasonal factors are not considered, the generalization and generalization of the established model will be poor, which will affect the prediction accuracy of the power station.

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