Wind power probability prediction method based on quantile regression

Abstract

The invention discloses a wind power probability prediction method based on quantile regression. The method comprises the steps of step 1, conducting CEEMDAN decomposition on all original wind power sequences si(n); step 2, carrying out normalization processing on the wind power sequence data after CEEMDAN decomposition; 3, training the model to obtain predicted values of the wind power at different quantiles at each moment in a period of time in the future; and step 4, adopting a kernel density estimation method for the prediction value of each moment to obtain each probability density distribution so as to predict the future wind power complete probability distribution. According to the method, more useful information than point prediction can be obtained, and prediction of future wind power integrity probability distribution is realized.

Description

technical field [0001] The invention relates to a method for predicting wind power probability based on quantile regression. Background technique [0002] With the increase of the proportion of wind power in the power grid, the shortcomings of wind power such as randomness and volatility are gradually amplified, which brings great challenges to the power grid under the condition of large-scale development of wind power. Accurately predicting wind power in advance can better guide grid power generation, scheduling, etc., and do a good job of preventing and eliminating wind power ramps and other wind power events that pose a greater threat to the grid. [0003] At present, there have been a lot of research on short-term wind power forecasting at home and abroad. In the statistical learning model, wind power forecasting is divided into point forecasting (deterministic forecasting) and interval forecasting (uncertainty forecasting). The current forecasting methods of point forec...

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Problems solved by technology

[0004] However, deterministic forecasting cannot quantitatively describe the uncertainty of wind power

In the field of power grid planning, operation, and security and stability analysis including wind power, it is necessary to have a more accurate estimate of the fluctuation range of wind power. It is not enough to obtain the predicted value of a single point. Uncertainty predictions need to assume a priori distribution, and The artificial selection distribution has a great influence on the results, and it is difficult to find a suitable prior distribution

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