Wind power prediction method based on self-learning composite data source autoregression model

Abstract

The invention discloses a wind power prediction method based on a self-learning composite data source autoregression model. The wind power prediction method based on the self-learning composite data source autoregression model comprises the steps that data are input to enable parameters of the autoregression model to be obtained; input data required by wind power prediction are input into the autoregression model determined according to the parameters of the autoregression model, so that a prediction result is obtained; post-evaluation is conducted on the prediction result, namely the error between a predicted value and a measured value is analyzed, and order determination of the autoregression model AR(p) and estimation of the parameters of the model AR(p) are conducted again if a predicted error is larger than an allowable maximum error. Key information is provided for new energy power generation real-time scheduling, a new energy power generation day-ahead plan, a new energy power generation monthly plan, new energy power generation capability evaluation and wind curtailment power estimation by predicting the wind power generated during wind power generation. The ultra-short-term wind power prediction accuracy is effectively improved due to the fact a composite data source is introduced, and thus the on-grid energy of new energy resources is effectively increased on the premise that safe, stable and economical operation of a power grid is guaranteed.

Description

technical field [0001] The present invention relates to the technical field of wind power forecasting in the process of new energy power generation, in particular to a wind power forecasting method based on self-learning composite data derived from a regression model. Background technique [0002] Most of the large-scale new energy bases generated after my country's wind power enters the stage of large-scale development are located in the "three north regions" (Northwest, Northeast, and North China). Large-scale new energy bases are generally far away from the load center, and their power needs to be transmitted to load center for consumption. Due to the intermittence, randomness and volatility of wind and light resources, the output of wind power and photovoltaic power generation in large-scale new energy bases will fluctuate in a large range, which will further lead to fluctuations in the charging power of the transmission network, which will affect the safety of power grid...

AI Technical Summary

Problems solved by technology

Due to the intermittence, randomness and volatility of wind and light resources, the output of wind power and photovoltaic power generation in large-scale new energy bases will fluctuate in a large range, which will further lead to fluctuations in the charging power of the transmission network, which will affect the safety of power grid operation. raises a series of questions

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