An ultra-short-term wind power forecasting method based on small wavelength short-term memory network

Abstract

The invention discloses an ultra-short-term wind power prediction method based on a small wavelength short-term memory network, which generates input variables according to historical data and takes the corresponding wind power historical data as output to obtain a training sample. Using wavelet analysis method to decompose the training samples into four wavelet samples, and using short-term and long-term memory network model to train the four wavelet samples respectively, the small-wavelength short-term memory network prediction model after training is obtained. According to the actual data of the four wavelet samples at the time to be predicted, the test input data are generated and input to the prediction model, and the output is the ultra-short-term wind power prediction value at the time to be predicted. The invention combines wavelet analysis method with long-term and short-term memory depth network, can realize data stabilization processing and depth learning at the same time, improves prediction accuracy and enhances model generalization ability.

Description

technical field [0001] The invention belongs to the technical field of new energy consumption, and in particular relates to an ultra-short-term wind power prediction method for predicting wind power. Background technique [0002] Nowadays, the decrease of fossil energy and the gradual increase of human demand for energy have become the main contradiction in the energy field. In order to solve the energy problem, human beings need to vigorously develop renewable energy, such as clean wind resources and solar energy resources. Due to the strong randomness, intermittency and volatility of resources, wind and light curtailment of renewable energy is still very serious. The forecasting research of renewable energy has become one of the key technologies for new energy consumption. With the advent of the era of artificial intelligence, more and more fields have successfully introduced advanced intelligent algorithms. For new energy forecasting research, it is even more necessary t...

AI Technical Summary

Problems solved by technology

However, in actual operation, if there are too many selected features or influencing factors, the structure of the predicted model may be complicated and the training efficiency will be reduced. The prediction accuracy is very important

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