Wind power short-term interval prediction method based on RT reconstructed EEMD-RVM combined model

Abstract

The invention discloses a wind power short-term interval prediction method based on an RT reconstructed EEMD-RVM combined model. Firstly, ensemble empirical mode decomposition is conducted on an original wind power sequence to obtain a stable intrinsic mode function (IMF) component and a remaining (RES) component with different features; by means of the runs-test method (RT), fluctuation degree detection is conducted on the components, and the similar components are reconstructed into three new components, with typical features, including a random component, a detail component and a trend component according to the fine-to-coarse sequence; then, a relevance vector machine (RVM) is adopted to the new components to build interval prediction models respectively; finally, prediction results of the new components are superposed to obtain a total interval prediction result under a certain confidence level. By means of the method, prediction precision of the models and the interval coverage are improved, the interval width is obviously reduced, and accordingly the prediction result is remarkably improved.

Description

technical field [0001] The invention belongs to the technical field of new energy power generation and smart grid, and relates to a wind power short-term interval prediction method based on RT reconstructed EEMD-RVM combination model. Background technique [0002] With the continuous development of industry, fossil energy is gradually decreasing, and environmental problems are becoming more and more serious. In order to alleviate the energy crisis and the pressure of environmental protection, it has become a consensus to develop and utilize clean and non-polluting renewable energy. Among them, wind power has attracted more and more attention and attention because of its clean and non-polluting, abundant reserves and recyclable utilization. Due to the randomness and volatility of natural wind, when wind power is connected to the grid on a large scale, the supply-demand balance and safe and stable operation of the grid will have a huge impact when large power fluctuations occu...

AI Technical Summary

Problems solved by technology

However, the ANN method can easily lead to insufficient learning or over-fitting problems during training; machine learning algorithms such as SVM can effectively avoid the risk of falling into a local minimum and can achieve more accurate predictions, but there are still the following deficiencies: ①The kernel function must satisfy Mercer conditions, fewer optional kernel functions; ②It can only achieve point prediction, but cannot describe the uncertain information of the data; ③There are many parameters, and the support vector increases linearly with the increase of training samples, and the calculation amount is large

At present, this method has been applied in the fields of load forecasting, fault classification, pattern recognition, etc., but there are few applications in wind power interval prediction.

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