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Renewable energy short-term prediction method based on data mining and variational mode decomposition

A technology of variational mode decomposition and renewable energy, which is applied in forecasting, neural learning methods, data processing applications, etc., can solve the problems of insufficient analysis and processing of renewable energy signals, single forecasting method, and insufficient extraction of relevant features for forecasting days

Pending Publication Date: 2020-03-24
ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY +1
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Problems solved by technology

But EMD has the disadvantage of frequent modal aliasing
[0005] To sum up, the current renewable energy forecasting usually has a single forecasting method, and there are the following challenges: 1) Insufficient extraction of relevant features on the forecast day
2) Insufficient signal analysis and processing of renewable energy
3) Renewable energy forecasting methods represented by traditional neural networks generally have problems such as long training time and overfitting

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  • Renewable energy short-term prediction method based on data mining and variational mode decomposition
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  • Renewable energy short-term prediction method based on data mining and variational mode decomposition

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[0102] Below in conjunction with accompanying drawing and embodiment the present invention will be further described:

[0103] In order to clearly illustrate the technical features of this solution, the present invention will be described in detail below through specific implementation modes and in conjunction with the accompanying drawings. The following disclosure provides many different embodiments or examples for implementing different structures of the present invention. To simplify the disclosure of the present invention, components and arrangements of specific examples are described below. Furthermore, the present invention may repeat reference numerals and / or letters in different instances. This repetition is for the purpose of simplicity and clarity and does not in itself indicate a relationship between the various embodiments and / or arrangements discussed. It should be noted that components illustrated in the figures are not necessarily drawn to scale. Description...

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Abstract

The invention discloses a renewable energy short-term prediction method based on data mining and variational mode decomposition. The renewable energy short-term prediction method comprises the following steps: 1), dividing a clustering process into data clusters: dividing the data clusters into some different groups in a given data set through a clustering algorithm, and extracting historical power generation data similar to environmental features into the same class; 2) processing historical data by variational modal decomposition: determining the bandwidth and the center frequency of each modal component by continuously iterating and searching the optimal solution of a variational model, and performing adaptive subdivision on the signal in a frequency domain; and 3) utilizing an extremelearning machine to predict future data. The technical scheme of the embodiment of the invention is combined with the K-means clustering technology, utilizes the variational mode decomposition and theextreme learning machine to predict the power generation amount of renewable energy, and has better accuracy compared with a traditional prediction method.

Description

technical field [0001] The invention relates to a short-term prediction method for renewable energy based on data mining and variational mode decomposition, belonging to the technical field of new energy power generation. Background technique [0002] With the depletion of traditional fossil fuels, renewable energy sources (RES) such as wind energy and solar energy are increasingly used in power systems. Microgrid (MG) can integrate various renewable energy sources into the power grid, usually composed of distributed energy sources, controllable loads, energy storage devices and other components, and has developed rapidly in recent years. However, due to the highly variable and intermittent nature of renewable energy, the reliability and stability of microgrids are difficult to guarantee. Accurate renewable energy forecasting is of great significance for improving the power quality of microgrids and strengthening the energy management of microgrids, and it can also effectiv...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06K9/62G06N3/04G06N3/08G06Q10/04G06Q50/06
CPCG06N3/08G06Q10/04G06Q50/06G06N3/045G06F18/23213G06F18/24
Inventor 杨冬邢鲁华周宁王亮李山张冰房俏张志轩蒋哲马欢李文博刘文学陈博赵康麻常辉
Owner ELECTRIC POWER RESEARCH INSTITUTE OF STATE GRID SHANDONG ELECTRIC POWER COMPANY
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