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Wind power prediction method based on a deep convolutional neural network

A technology of wind power prediction and deep convolution, applied in biological neural network models, predictions, neural architectures, etc., can solve problems such as increased computational costs, inability to express wind spatiotemporal changes, and limited wind power prediction, etc., to achieve extended expression capabilities Effect

Active Publication Date: 2019-04-19
TIANJIN UNIV
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

In short, the above work is still based on time series data modeling in essence. The accuracy rate is improved through complex models, but the calculation cost is also significantly increased. However, in fact, these time series cannot express the spatiotemporal change process of wind. limits the level of wind power forecasting

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  • Wind power prediction method based on a deep convolutional neural network
  • Wind power prediction method based on a deep convolutional neural network
  • Wind power prediction method based on a deep convolutional neural network

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Embodiment 1

[0045] An embodiment of the present invention provides a wind power prediction method based on a deep convolutional neural network, see figure 1 , the method includes:

[0046] 101: Select and collect wind farm data, and use the grid space embedding method to map the real coordinates of the wind turbine to the plane grid;

[0047] Wherein, the grid space embedding method is well known to those skilled in the art, and the embodiment of the present invention does not repeat it here.

[0048] The steps of selecting and collecting wind farm data are specifically: selecting a wind farm area within a certain range of latitude and longitude, and collecting historical data of all (a total of n) wind turbines in the wind farm area, with a fixed time interval. Based on the above data, predict the wind power output of the wind turbine after a period of time.

[0049] Among them, using the grid space embedding method to map the real coordinates of the wind turbine to the plane grid, the...

Embodiment 2

[0060] Combine below Figure 2-Figure 4 The scheme in Example 1 is further introduced, see the following description for details:

[0061] The embodiment of the present invention proposes STF to represent the information of the state of the wind farm, and proposes three STF-based deep convolutional neural network models to accurately and efficiently predict wind power, such as figure 1 As shown, it is an overall schematic diagram of a specific embodiment of wind power prediction using a deep convolutional neural network model in an embodiment of the present invention, including:

[0062] 201: Select the longitude range as x 1 ~x 2 , the dimension range is y 1 ~y 2 wind farm area, and collect historical data such as wind speed and power of n wind turbines in the wind farm area, with a fixed time interval;

[0063] 202: Preprocessing the data collected in step 201 to form continuous time series historical data for each wind turbine in the wind farm;

[0064] Wherein, based...

Embodiment 3

[0089] Combined with the calculation formula below, Figure 5 , and table 1 carries out feasibility verification to the scheme in embodiment 1 and 2, see the following description for details:

[0090] Accuracy is the most important aspect to measure the effect of wind power forecasting, and the main indicators for evaluating accuracy are mean square error (MSE) and root square error (RMSE). Wherein, RMSE is the arithmetic square root of MSE, so the embodiment of the present invention selects MSE as the evaluation standard of wind power prediction. The calculation method of MSE is shown in formula (1), where real is the real value sequence, predictions is the predicted value sequence, and n is the sequence length.

[0091]

[0092] The MSE is calculated for the prediction results of each model, and the prediction effects of the three models are compared and analyzed, and then the advantages and disadvantages of the models, the temporal and spatial characteristics of wind p...

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Abstract

The invention discloses a wind power prediction method based on a deep convolutional neural network, and the method comprises the following steps: selecting and collecting the data of a wind power plant, and enabling the real coordinates of a wind driven generator to be mapped to a plane grid through employing a grid space embedding method; Filling the output of all wind turbines in the wind powerplant at a certain moment into a grid according to a mapping result to obtain scene characteristics corresponding to the moment, and arranging a plurality of continuous scene characteristics according to a time sequence to form a multi-channel image, namely, a space-time characteristic; Constructing three deep convolutional network models on the basis of the space-time characteristics to predictthe wind power; And analyzing and comparing the wind power prediction effect of each model. According to the method, STF in a multi-channel image form is constructed by embedding a grid space of a wind turbine in a wind power plant area, and the space-time transformation process of air flow is fully expressed; Three deep convolutional network models are provided, and each model can predict a largenumber of wind power of the wind turbine at the same time.

Description

technical field [0001] The invention relates to the technical field of wind power control, in particular to a wind power prediction method based on a deep convolutional neural network. Background technique [0002] With the continuous development of the global economy, people's demand for energy is also increasing, and the problems of energy and environment have attracted great attention from the international community and the public. However, coal, oil, natural gas and other energies that people have relied on for a long time are non-renewable energy sources. The use of coal and oil will cause serious pollution to the environment and restrict the sustainable development of human beings. In order to solve the problems of energy and environment, people continue to develop new energy sources to promote the sustainable development of the global economy and cope with global climate change. New energy includes solar energy, wind energy, ocean energy, geothermal energy, etc. Amo...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/04
CPCG06Q10/04G06Q50/06G06N3/045Y04S10/50
Inventor 于瑞国刘志强李雪威路文焕喻梅王建荣李斌马德刚
Owner TIANJIN UNIV
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