Method and device for forecasting combined output of multiple wind farms

Abstract

The invention discloses a method and device for predicting the combined output of multiple wind farms based on a dynamic R-Vine Copula model, belonging to the field of wind power interval prediction in a power system. The method includes: S1: combining the predicted output data of multiple wind farms with The prediction error corresponding to the joint output is used as the first input data; S2: Input the first input data into the dynamic marginal distribution function model established based on the ARIMA-GARCH model, so that the dynamic marginal distribution function model converts the first input data into a cumulative probability sequence ; S3: Input the cumulative probability sequence into the pre-established dynamic R‑Vine Copula model, so that the dynamic R‑Vine Copula model outputs the joint output prediction results corresponding to multiple wind farms under different confidence levels; wherein, the model of the dynamic marginal distribution function model The parameters and model parameters of the dynamic R‑Vine Copula model are calculated and updated on a rolling basis based on the phase space reconstruction method. The present application can improve the ultra-short-term interval prediction accuracy of the combined output of multiple wind farms.

Description

technical field [0001] The invention belongs to the field of wind power interval prediction in a power system, and more particularly relates to a method and device for predicting the combined output of multiple wind farms based on a dynamic R-Vine Copula model. Background technique [0002] At present, my country's wind power installed grid-connected capacity continues to grow steadily, which strongly promotes the transformation of the existing power supply and demand structure to a green and low-carbon direction. However, the accurate forecast of wind power is still difficult to achieve. In the context of the rapid growth of installed capacity, the limited forecast accuracy makes the uncertainty of wind power difficult to ignore. In order to deal with the above problems, interval prediction of wind power is an effective method. Different from traditional point forecasting methods, interval forecasting can provide complete probability distribution information of wind power,...

AI Technical Summary

Problems solved by technology

[0005] Aiming at the above defects or improvement needs of the prior art, the present invention provides a method and device for predicting the joint output of multiple wind farms based on the dynamic R-VineCopula model, aiming at solving the problem that the existing model cannot fully consider the joint output of multiple wind farms. Dimensional and time-varying correlation characteristics to improve the accuracy of ultra-short-term interval forecasting of joint output of multiple wind farms

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