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Wind power interval prediction method based on atom decomposition and interactive fuzzy satisfaction

A technology of atomic decomposition and prediction method, which is applied in prediction, character and pattern recognition, instruments, etc., and can solve problems such as uncertainty and random error

Inactive Publication Date: 2018-03-13
WUHAN UNIV
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Problems solved by technology

Most of these methods focus on deterministic point prediction, and their random errors bring certain uncertainty to the prediction.

Method used

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  • Wind power interval prediction method based on atom decomposition and interactive fuzzy satisfaction
  • Wind power interval prediction method based on atom decomposition and interactive fuzzy satisfaction
  • Wind power interval prediction method based on atom decomposition and interactive fuzzy satisfaction

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Embodiment Construction

[0088] In order to facilitate those of ordinary skill in the art to understand and implement the present invention, the present invention will be described in further detail below in conjunction with the accompanying drawings and embodiments. It should be understood that the implementation examples described here are only used to illustrate and explain the present invention, and are not intended to limit this invention.

[0089]In this embodiment, the matlab programming simulation platform is used to test the wind power data of a certain wind farm for half a year, and the sampling time interval of the wind farm is 15 minutes. Each set of experiments selects the data of a continuous month of wind farm normal operation for simulation, and the number of samples in the training set, validation set, and test set are 2080, 400, and 400, respectively. The parameters of the algorithm are set as follows: the maximum number of iterations of the TLBO algorithm is 200, and the number of s...

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Abstract

The invention discloses a wind power interval prediction method based on atom decomposition and interactive fuzzy satisfaction. According to the method, an improved atom sparse decomposition method isused to decompose an original wind power sequence into a series of subsequences; each subsequence is subjected to sample entropy calculation, and then the subsequences are recombined into a random component, a cyclic component and a trend component according to sample entropy of each subsequence; a prediction model is established through a core extreme learning machine for each component, and meanwhile a wind power interval objective function based on interactive fuzzy satisfaction is established considering coverage, average bandwidth and bandwidth deviation of a wind power interval; and a teaching and learning algorithm is used to perform optimization, and an optimal wind power probability interval is obtained through prediction. Through the method, the problem that a traditional wind power prediction method cannot reflect uncertainty of a prediction result is solved.

Description

technical field [0001] The invention belongs to the field of wind power generation output prediction, and relates to a wind power interval prediction method, in particular to a multi-objective wind power interval prediction method based on atomic sparse decomposition and interactive fuzzy satisfaction. Background technique [0002] Wind power is widely used because of its clean and non-polluting characteristics, and its proportion in the power grid will become larger and larger. However, its intermittency and instability pose challenges to the safe and stable operation of the power grid. Accurate and reliable prediction of wind farm power is one of the effective means to help reduce system reserve capacity and reduce the impact of wind power access on the grid. At present, the research on wind power forecasting methods can be divided into physical methods, statistical methods, combination methods and so on. Most of these methods focus on deterministic point prediction, and ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06K9/62G06F17/50
CPCG06Q10/04G06Q50/06G06F30/20G06F18/211
Inventor 胡志坚胡梦月
Owner WUHAN UNIV
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