Wind speed prediction method based on time sequence abrupt change error correction
A time series and error correction technology, applied in the field of wind speed prediction based on time series mutation error correction, can solve problems such as the influence of grid stability and disrupt the normal operation of the power system, and achieve the effect of reducing operating costs, reducing impact and improving accuracy
Active Publication Date: 2020-06-02
HUBEI UNIV OF TECH
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Problems solved by technology
Due to the volatility and intermittent nature of wind power, when large-scale wind power is connected to the grid, it may affect the stability of the grid, and even disrupt the normal operation of the power system
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[0033] Embodiments of the present invention will be described in detail below in conjunction with the accompanying drawings.
[0034] This embodiment discloses an EMD-LMD-GWO-SVM wind speed prediction method based on time series mutation error correction, including prediction and error correction.
[0035] Firstly, the wind speed data is decomposed into a series of relatively stable components by using empirical mode decomposition and local mean value decomposition, and the components are predicted by GWO-SVM to obtain the prediction results of the components, and the original wind speed data is obtained after integrating the prediction results of all components Then, according to the errors existing in the prediction, the classification and analysis of error types are carried out, and a method based on time series mutation error correction is proposed to correct the wind speed prediction results. This method can significantly reduce the wind speed prediction. error.
[0036]...
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The invention belongs to a wind power generation technology, and particularly relates to a wind speed prediction method based on time sequence abrupt change error correction. The method comprises thefollowing steps: decomposing wind speed data into a series of relatively stable components by utilizing empirical mode decomposition and local mean decomposition respectively, predicting the components by adopting GWO-SVM respectively to obtain prediction results of the components, and integrating the prediction results of all the components to obtain a prediction result of original wind speed data; then, aiming at an error existing in prediction, classifying and analyzing error types, and performing error correction on a wind speed prediction result. According to the method, the errors of wind speed prediction can be remarkably reduced; therefore, high-precision wind power generation power prediction is provided, a power grid is facilitated to adjust a power dispatching plan in time, efficient utilization of wind energy resources is improved, the operation cost of the power grid is reduced, the influence on the power grid when wind power is accessed to the power grid is reduced, and positive and important significance is achieved for development of wind power generation.
Description
technical field [0001] The invention belongs to the technical field of wind power generation, and in particular relates to a wind speed prediction method based on time series mutation error correction. Background technique [0002] As the world attaches great importance to the development of green energy, wind energy, as a green and renewable energy, has been vigorously developed and utilized. Due to the volatility and intermittent nature of wind power, when large-scale wind power is connected to the grid, it may have an impact on the stability of the grid, and even disrupt the normal operation of the power system. Therefore, providing high-precision wind power forecasting will help the grid to adjust power dispatching plans in a timely manner, improve the efficient use of wind energy resources, reduce grid operating costs, and reduce the impact of wind power on the grid when connected to the grid. Positive significance. [0003] Wind speed forecasting is one of the main f...
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IPC IPC(8): G06Q10/04G06Q50/06G06N20/10G06N3/00
CPCG06Q10/04G06Q50/06G06N20/10G06N3/006
Inventor 黄文聪张隽怡谢博张宇常雨芳陈润段群龙李金榜吴锋
Owner HUBEI UNIV OF TECH