High-precision wind electric field power interval forecasting method based on relevance vector machine

Abstract

The invention discloses a high-precision wind electric field power interval forecasting method based on a relevance vector machine, which comprises the steps of collecting data, carrying out normalization on the data, and selecting a training sample of a relevance vector machine forecasting model; optimizing parameters of the relevance vector machine forecasting model to obtain an optimized iteration initial value of kernel function width and the relevance vector machine forecasting model; obtaining a kernel function, and then obtaining relevance vector machine forecasting model parameters after convergence; and finally obtaining a forecasting value and a variance of a wind electric field, so as to obtain a forecasted interval of wind electric field power. The method can improve adaptability of the model, improve forecasting accuracy, reduce training sample size and reduce training time.

Description

technical field [0001] The invention belongs to the technical field of wind farms, and in particular relates to a high-precision wind farm power interval prediction method based on a correlation vector machine. Background technique [0002] With the leapfrog development of wind power generation, the inherent intermittent and random fluctuations of wind power generation seriously threaten the economy, stability and power supply quality of power system operation. Wind farm power forecasting technology is one of the effective ways to alleviate the adverse impact of wind farm grid-connected on the power system. Accurate and reliable power forecasting of wind farms can also help: (1) reduce spinning reserve capacity and rationally arrange maintenance plans, thereby reducing operating costs; (2) increase the proportion of wind power connected to the grid; (3) improve the competitiveness of wind power companies and provide wind power Online bidding provides favorable conditions. ...

AI Technical Summary

Problems solved by technology

[0010] Aiming at the deficiencies of the existing wind power prediction methods mentioned in the above background technology, such as weak generalization ability and inability to provide uncertainty information, the present invention proposes a high-precision wind farm power interval prediction method based on correlation vector machine

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