Wind power non-parametric probability interval ultra-short-term prediction method

Abstract

The invention discloses a wind power non-parametric probability interval ultra-short-term prediction method. The method is based on adaptive LASSO and an extreme learning machine. Firstly, nonlinear quantile regression is carried out on a wind power sequence to obtain self-adaptive adjustment parameters; an optimal quantile regression model output coefficient is calculated based on an extreme learning machine by utilizing quantile regression based on adaptive LASSO and an improved Bayesian information criterion; and finally, a wind power time sequence is input to obtain an ultra-short-term prediction value. According to the quantile regression prediction model constructed through the method, the interval score is obviously superior to that of a traditional prediction model based on quantile regression, the prediction precision and interval width comprehensive indexes are good, and the reliability of wind power prediction is greatly improved.

Description

technical field [0001] The invention belongs to the technology of new energy power generation and smart grid, and specifically relates to a non-parametric probability interval ultra-short-term prediction method of wind power power, in particular to an ultra-short-term prediction method of wind power non-parametric probability interval based on adaptive LASSO and extreme learning machine . Background technique [0002] With the continuous advancement of technology, wind energy has become the most important sustainable energy. However, the randomness and volatility of wind power generation in the current technology restrict its application and development. Traditional wind power forecasting mainly focuses on point forecasting. However, due to the uncertainty and complexity of wind power, forecasting errors are unavoidable. In view of this, more and more technicians pay attention to the method of probability interval forecasting. Different from the point prediction method th...

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Problems solved by technology

At present, the method based on quantile regression still has some shortcomings, such as the output coefficient value obtained by training is too large, and the training is easily affected by some irrelevant or irrelevant parameters.

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