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TCN-based wind power prediction error interval evaluation method

A technology for forecasting errors and wind power, applied in forecasting, neural learning methods, instruments, etc., can solve problems such as unclear physical meaning of results, insufficient combination of wind power signal characteristics, etc., and achieve the effect of avoiding gradient disappearance

Inactive Publication Date: 2020-03-31
徐州上若科技有限公司
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Problems solved by technology

However, the parameter selection in these decomposition methods largely depends on experience, and the combination with the characteristics of the wind power signal itself is insufficient, and the physical meaning of the decomposition results is not clear

Method used

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  • TCN-based wind power prediction error interval evaluation method
  • TCN-based wind power prediction error interval evaluation method
  • TCN-based wind power prediction error interval evaluation method

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Embodiment Construction

[0037] Such as Figure 1 to Figure 7 A TCN-based wind power prediction error interval evaluation method is shown, taking the actual wind power data of NREL Corporation of the United States as an example, including the following steps:

[0038] Step 1: Obtain the raw data P(t) of wind power and normalize it, and divide it into training set and test set;

[0039] Step 2: Initialize the weight of the TCN network, set the number of iterations, the number of residual modules and the number of network layers, the expansion coefficient, the size of the convolution kernel, the learning rate and the number of neurons in the hidden layer. The specific steps are as follows:

[0040] Step 2.1: Using a causal convolutional CNN model, the sequence problem can be transformed into: according to x 1 , x 2 ,...,x t to predict y 1 ,y 2 ,...,y t , the definition of causal convolution, filter F=(f1 , f 2 ,...,f k ), sequence X=(x 1 , x 2 ,...,x T ), at x t The causal convolution at is:...

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Abstract

The invention relates to a TCN-based wind power prediction error interval evaluation method, which comprises the steps of obtaining and normalizing wind power original data, and dividing the wind power original data into a training set and a test set; initializing the weight of the TCN network, and setting the number of iterations, the number of residual modules, the number of network layers, an expansion coefficient, a convolution kernel size, a learning rate and parameters of the number of hidden layer neurons; inputting the training set data into the TCN in batches, calculating an output error of an effective historical length, and performing back propagation on the error to update TCN parameters; completing the training until the TCN network precision meets the artificially given erroror reaches the number of iterations; and inputting test data and outputting a prediction result to realize real-time error prediction of the wind power. Error prediction can be realized by means of the flexible receptive field and learning capability of the TCN, thereby providing a basis for real-time scheduling.

Description

technical field [0001] The invention relates to a TCN-based wind power prediction error interval evaluation method, which belongs to the technical field of electric power system control. Background technique [0002] The prediction of wind power has always been a technical problem, and the short-term prediction error can even reach 40%. To this end, it is necessary to take various measures, such as various backup, energy storage, and even energy Internet as compensation, to reduce the risk brought by wind power. On the one hand, this restricts the large-scale access of wind power, and on the other hand, it exacerbates the problem of wind curtailment. If the interval evaluation of the prediction error is provided while predicting the wind power, the operator of the power grid can correct the power generation plan of each power plant in the grid or the output of other forms of energy in the Energy Internet according to the predicted value of wind power and the prediction erro...

Claims

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Application Information

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IPC IPC(8): G06Q10/04G06Q50/06G06N3/08
CPCG06Q10/04G06Q50/06G06N3/084
Inventor 韩丽景惠甜高志宇史丽萍
Owner 徐州上若科技有限公司
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