Short-term wind power prediction method and system based on deep learning network

A technology for wind power forecasting and deep learning, applied in neural learning methods, forecasting, biological neural network models, etc., can solve problems such as too many human resources and computing resources

Active Publication Date: 2021-02-09
CHINA ELECTRIC POWER RES INST
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  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

Using the accumulation method for regional wind power prediction requires the establishment of a model for each w

Method used

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  • Short-term wind power prediction method and system based on deep learning network
  • Short-term wind power prediction method and system based on deep learning network
  • Short-term wind power prediction method and system based on deep learning network

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Effect test

Embodiment 1

[0056] A schematic flow chart of a short-term wind power forecasting method based on a deep learning network provided by the present invention is as follows figure 1 shown, including:

[0057] S1: Obtain the numerical weather prediction data of the area where the wind power to be predicted is located;

[0058] S2: Input the numerical weather forecast data into the pre-trained deep learning mapping model to obtain the predicted value of wind power;

[0059] Among them, the deep learning mapping model includes the corresponding relationship between the numerical weather prediction data and the wind power prediction value; the numerical weather prediction data forms a grid according to the position, and each grid point in the grid includes multiple weather parameters.

[0060] Specifically, the present invention specifically includes:

[0061] Step 1: Organize the regional numerical weather prediction data.

[0062] Organize the obtained regional numerical weather prediction d...

Embodiment 2

[0087] Based on the same inventive concept, the present invention also provides a short-term wind power forecasting system based on a deep learning network.

[0088] The basic structure of the system is as Figure 4 As shown, including: data acquisition module and wind power prediction module;

[0089] The data collection module is used to obtain the numerical weather prediction data of the area where the wind power to be predicted is located;

[0090] The wind power prediction module is used to input the numerical weather prediction data into the pre-trained deep learning mapping model to obtain the predicted value of wind power;

[0091] Among them, the deep learning mapping model includes the corresponding relationship between the numerical weather prediction data and the wind power prediction value; the numerical weather prediction data forms a grid according to the position, and each grid point in the grid includes multiple weather parameters.

[0092] The detailed stru...

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Abstract

The invention provides a short-term wind power prediction method and system based on a deep learning network. The short-term wind power prediction method comprises the steps of obtaining numerical weather forecast data of an area where to-be-predicted wind power is located; inputting the numerical weather forecast data into a pre-trained deep learning mapping model to obtain a predicted value of the wind power, wherein the deep learning mapping model comprises a corresponding relationship between the numerical weather forecast data and the wind power prediction value; and forming a grid by thenumerical weather forecast data according to positions, wherein each grid point in the grid comprises a plurality of weather parameters. According to the invention, the short-term power prediction precision of the wind power plant can be improved, meanwhile, the modeling time of the regional wind power prediction model can be shortened, and required computing resources and manpower resources arereduced.

Description

technical field [0001] The invention belongs to the technical field of clean energy consumption, and in particular relates to a short-term wind power prediction method and system based on a deep learning network. Background technique [0002] With the maturity of wind power generation technology, the capacity of wind power units and the scale of grid-connected wind farms continue to expand, and the proportion of wind power in the total power generation of the power system is also increasing year by year. The penetrating power of wind farms continues to increase, which brings a series of problems to the power system that are increasingly prominent, seriously threatening, and the power system is safe, stable, economical and reliable. Timely and accurate prediction of wind power can significantly enhance the safety, stability, economy and controllability of the power system, and enhance the ability to accommodate wind power. [0003] The existing short-term wind power forecast...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/08G06N3/04
CPCG06Q10/04G06Q50/06G06N3/08G06N3/045
Inventor 裴岩王勃车建峰冯双磊刘纯汪步惟王铮王钊赵艳青姜文玲张菲
Owner CHINA ELECTRIC POWER RES INST
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