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Wind power prediction method and device, equipment and storage medium

A technology of wind power forecasting and clustering algorithm, applied in forecasting, circuit devices, wind power generation, etc., can solve the problems of complex nonlinear relationship, inability to explain the nonlinear relationship of wind power, and many influencing factors of wind power forecasting, so as to improve the performance of wind power. The effect of precision

Pending Publication Date: 2022-04-08
GUANGDONG POWER GRID CO LTD +1
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] In addition, there are many influencing factors in wind power prediction, and the nonlinear relationship is complicated. However, due to the number of parameters and the complex methods of extracting and combining features, the existing deep learning models cannot explain the non-linear relationship between wind power and multiple influencing factors. linear relationship

Method used

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  • Wind power prediction method and device, equipment and storage medium
  • Wind power prediction method and device, equipment and storage medium
  • Wind power prediction method and device, equipment and storage medium

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Experimental program
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Embodiment 1

[0043] Figure 1a It is a flow chart of a wind power prediction method in Embodiment 1 of the present invention. This embodiment is applicable to the situation of predicting wind power according to the wind energy data to be measured. This method can be executed by a wind power prediction device, which can be composed of It can be implemented by hardware and / or software, and can generally be integrated in computer equipment that provides wind power forecasting functions. Specifically, refer to Figure 1a , the method may include the following steps:

[0044] Step 110, collect sample data, and perform feature extraction on the sample data to obtain a model input feature data set.

[0045] In this embodiment, the sample data includes wind speed data, wind direction data, air temperature data and wind power. Exemplarily, the sample data can come from the wind speed data, wind direction data, air temperature data and power data of a wind turbine in a wind farm in a certain area f...

Embodiment 2

[0065] figure 2 It is a schematic structural diagram of a wind power prediction device in Embodiment 2 of the present invention. This embodiment is applicable to the situation of predicting wind power according to the wind energy data to be measured. The device can be realized by hardware and / or software, and generally can be Integrated in computer equipment that provides wind power forecasting functions. Specifically, refer to figure 2 , the device can include:

[0066] The feature extraction module 210 is used to collect sample data, and perform feature extraction on the sample data to obtain a model input feature data set;

[0067] Cluster analysis module 220, for adopting k-means clustering algorithm to carry out cluster analysis to model input characteristic data set, and the data category gained by clustering is added to model input characteristic data set as new feature;

[0068] The model training module 230 is used to train the preset generalized additive model a...

Embodiment 3

[0086] image 3 It is a schematic structural diagram of a computer device in Embodiment 3 of the present invention. image 3 A block diagram of an exemplary device 12 suitable for use in implementing embodiments of the invention is shown. image 3 The shown device 12 is only an example and should not impose any limitation on the functions and scope of use of the embodiments of the present invention.

[0087] Such as image 3 As shown, device 12 takes the form of a general purpose computing device. Components of device 12 may include, but are not limited to: one or more processors or processing units 16, system memory 28, bus 18 connecting various system components including system memory 28 and processing unit 16.

[0088] Bus 18 represents one or more of several types of bus structures, including a memory bus or memory controller, a peripheral bus, an accelerated graphics port, a processor, or a local bus using any of a variety of bus structures. These architectures inclu...

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Abstract

The embodiment of the invention discloses a wind power prediction method and device, equipment and a storage medium. The method comprises the steps of collecting sample data, and performing feature extraction on the sample data to obtain a model input feature data set; performing clustering analysis on the model input feature data set by adopting a k-means clustering algorithm, and adding a data category obtained by clustering into the model input feature data set as a new feature; training a preset generalized additive model according to the model input feature data set to obtain a wind power prediction model; and inputting to-be-tested data into the wind power prediction model to obtain a data category of the to-be-tested data and a prediction result of the wind power. According to the technical scheme provided by the embodiment of the invention, the influence of each variable on the wind power is explained through the generalized additive model, and the precision of wind power prediction is effectively improved in combination with clustering analysis.

Description

technical field [0001] The embodiments of the present invention relate to the technical field of artificial intelligence, and in particular to a wind power prediction method, device, equipment, and storage medium. Background technique [0002] In recent years, with the rapid development of the wind power industry, the installed capacity of wind power is increasing on a large scale. However, the randomness and volatility of wind power generation will have a huge impact on the stability of the power system after it is connected to the grid. If the output of wind power can be predicted for a period of time in advance, it will effectively improve the utilization rate of wind energy and help dispatchers to arrange dispatching plans reasonably, thereby ensuring the safe operation of the power grid. Therefore, the research on the short-term wind power forecasting algorithm is of great significance. [0003] In addition, there are many influencing factors in wind power prediction,...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06K9/62H02J3/38H02J3/00
CPCY02E10/76
Inventor 肖建华龚贤夫罗苑萍刘冬明傅惠芹刘满黄雄斌丁朋
Owner GUANGDONG POWER GRID CO LTD
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