Super short-term wind power forecasting method based on back propagation (BP) neural network

Abstract

The invention relates to a super short-term wind power forecasting method based on a back propagation (BP) neural network, which is technically characterized by comprising the steps that: data is grouped through a cross grouping method as the input of a training process of the BP neural network; the training of the BP neural network is carried out through modified learning rate algorithm; and a forecasting result is obtained by calculating the wind power of a wind farm. The super short-term wind power forecasting method based on the BP neural network has a reasonable design, adopts the improved algorithm of the BP neural network, solves the problem that the convergence rate is slow during the training process by modifying the learning rate in the training process, can greatly improve the convergence rate in the training process, simultaneously selects data to train the neural network through the cross grouping method, can approach any continuous non-linear function by any precision; and with the increased scale of the wind farm and the non-linear increase of the wind speed changes, the adopted wind power forecasting method has very good robustness.

Description

technical field [0001] The invention belongs to the field of wind power generation, in particular to an ultra-short-term wind power prediction method based on BP neural network. Background technique [0002] The dispatch of wind power generation needs the support of ultra-short-term wind power forecasting technology. The definition of ultra-short-term wind power prediction is: the output power prediction of wind farms in the future 15min-4h, with a time resolution of 15min, and its corresponding English expression is Super short term wind power prediction. [0003] The prediction object of wind speed and wind farm power belongs to a complex nonlinear process involving many factors, and has a high degree of uncertainty. The traditional wind power prediction method uses the time series method to predict wind speed, and then predicts wind power according to the wind speed. Affects the error of wind speed prediction. Since the time series model is a linear model, the effect of...

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Problems solved by technology

[0004] The purpose of the present invention is to overcome the deficiencies in the prior art, provide a kind of ultra-short-term wind power prediction method based on BP neural network, this method solves the problem that the convergence speed of the training process is slow by modifying the size of the learning rate in the training process, Overcome the non-linear problem in wind speed prediction

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