Wind power plant short-term wind power prediction method based on VMD-FCM-GRU

A wind power forecasting and wind power technology, applied in forecasting, electrical digital data processing, instruments, etc., can solve problems such as long training and forecasting time, non-convergence of model training, and complex LSTM structure

Pending Publication Date: 2020-10-30
NORTHEASTERN UNIV
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Problems solved by technology

However, LSTM has a complex structure and requires a long training and prediction time
When the training samples are insufficient or

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  • Wind power plant short-term wind power prediction method based on VMD-FCM-GRU
  • Wind power plant short-term wind power prediction method based on VMD-FCM-GRU
  • Wind power plant short-term wind power prediction method based on VMD-FCM-GRU

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Example Embodiment

[0078] The specific embodiments of the present invention will be described in further detail below in conjunction with the drawings and embodiments. The following examples are used to illustrate the present invention, but not to limit the scope of the present invention.

[0079] In this embodiment, the neural network model is built with Google's open source deep learning framework Tensorflow1.6 as the backend, Keras2.0 is the high-level API, the machine learning model is built as Scikit-Learn1.9, and the programming language is python3.6. This embodiment is initialized reasonably based on experience, and adjusted based on the training results.

[0080] Such as figure 1 As shown, the method of this embodiment is as follows.

[0081] Step 1: Collect data of wind power p and wind speed v, and perform preprocessing including: removing abnormal data, supplementing missing values, and normalizing to obtain the normalized wind power sequence X′ i ;

[0082] In this embodiment, actual opera...

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Abstract

The invention discloses a wind power plant short-term wind power prediction method based on VMD-FCM-GRU, and belongs to the technical field of wind power prediction. The method comprises the followingsteps: firstly, obtaining a series of finite stable subsequence components based on a VMD normalized complex wind power time sequence, clustering the subsequence components by using an FCM algorithm,clustering the subsequences with similar fluctuation trends, and superposing the subsequences so as to shorten the training time; and training a GRU neural network for prediction by using the clustered sequences, and superposing and reversely normalizing prediction results of the sub-sequences to obtain a final prediction result. According to the method, in combination with the strong calculationefficiency of the GRU, the VMD-FCM-GRU combined model can realize relatively good prediction precision and relatively short training time, and the precision and speed of short-term wind power prediction can be remarkably improved.

Description

technical field [0001] The invention relates to the technical field of wind power forecasting, in particular to a VMD-FCM-GRU-based short-term wind power forecasting method for wind farms. Background technique [0002] With the reduction of world energy, the research and development and utilization of renewable energy have become an important development strategy. With the advantages of wide range, renewable and non-polluting, wind energy has gradually become the most promising energy source. The inherent randomness and volatility of wind power generation creates an imbalance between wind power supply and load demand. Improving the prediction accuracy of wind power is an effective way to solve these problems, and it is also a research hotspot for a long time. [0003] Wind forecasting technology can be divided into two categories according to the form of forecasting results: uncertain forecasting and definite forecasting. Uncertain forecasting methods are usually based on...

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Application Information

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IPC IPC(8): G06Q10/04G06F30/20G06F30/27G06K9/62G06Q50/06G06F113/06
CPCG06Q10/04G06F30/20G06F30/27G06Q50/06G06F2113/06G06F18/23
Inventor 桑春旭杨东升周博文张化光金硕巍闫士杰罗艳红刘鑫蕊杨波孙振奥梁雪刘振伟王智良
Owner NORTHEASTERN UNIV
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