Double-layer collaborative real-time correction photovoltaic prediction method

Abstract

The invention relates to the technical field of photovoltaic system ultra-short-term power prediction. In order to solve the problem of low accuracy of ultra-short-term prediction of a photovoltaic system in case of changeable weather, the invention provides a double-layer collaborative real-time correction photovoltaic prediction method, which comprises the following steps of: obtaining a reference layer photovoltaic prediction set in the future N hours by utilizing a reference layer photovoltaic prediction model F1; in combination with the photovoltaic prediction error of the reference layer, utilizing a real-time layer photovoltaic prediction model F2 to correct prediction values in the reference layer photovoltaic prediction set one by one, so that a final real-time layer photovoltaicprediction set can be obtained; according to a photovoltaic fluctuation rule, considering an optimal time sequence translation characteristic, and obtaining a final photovoltaic power prediction valueafter a photovoltaic prediction result of a reference layer is corrected, so that the influence of a process weather factor on photovoltaic prediction can be weakened, and the photovoltaic predictionprecision is improved.

Description

technical field [0001] The invention relates to the technical field of photovoltaic ultra-short-term power forecasting, and specifically relates to a double-layer collaborative real-time correction photovoltaic forecasting method to weaken the influence of procedural weather factors on photovoltaic forecasting and improve the accuracy of ultra-short-term photovoltaic forecasting. Background technique [0002] The existing ultra-short-term photovoltaic forecasting methods are mainly divided into two categories: one is to use time series analysis to estimate the change law of photovoltaic power based on historical photovoltaic data information. For example, the ultra-short-term photovoltaic power prediction method of three-stage time series modal decomposition, constructing different time scale photovoltaic power mean series to establish a local power prediction model 1h in advance, and performing photovoltaic power prediction based on similar periods of screening, within 1-2h ...

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Problems solved by technology

[0004] The XGBoost algorithm has a good performance in solving classification, regression, prediction and other problems, but it has less research on photovoltaic prediction with a time scale of ultra-short term, and is still limited to classical feature learning or time series transition models.

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