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An integrated method for multiple prediction results of electric load probability density

A technology of probability density and power load, applied in forecasting, data processing applications, instruments, etc.

Active Publication Date: 2021-04-13
TSINGHUA UNIV
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  • Abstract
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  • Application Information

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Problems solved by technology

In the field of electric load forecasting, there is no related research on the integration of probability density forecasting

Method used

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  • An integrated method for multiple prediction results of electric load probability density
  • An integrated method for multiple prediction results of electric load probability density
  • An integrated method for multiple prediction results of electric load probability density

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Embodiment Construction

[0066] The integration method of various forecasting results of the electric load probability density proposed by the present invention, its flow chart is as follows figure 1 shown, including the following steps:

[0067] (1) The historical power load data D = [d 1 , d 2 ,... d t ,... d T ] is divided into three parts according to the set ratio, generally speaking, the ratio setting of the first part is larger than the sum of the ratios of the second part and the third part, and the ratio is 10:1:1 in one embodiment of the present invention The three parts of data are recorded as: training set D 1 , validation set D 2 and combination set D 3 ,

[0068]

[0069] Among them, the data set D 1 is of length T 1 , data set D 2 is of length T 2 , data set D 3 The length is T-T 1 -T 2 , in one embodiment of the present invention [ ] is the rounding down function;

[0070] (2) Using different hyperparameters, train D separately 1 The three probability prediction mo...

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Abstract

The invention relates to an integration method of multiple prediction results of electric load probability density, belonging to the technical field of power system analysis. The present invention obtains a plurality of probability densities or quantile probability prediction models through the training of three types of regression models set by multiple sets of different hyperparameters, and converts the output of the quantile prediction models into obedience A probability density model for a Gaussian distribution. Using the integrated method of probability density prediction, the optimal integrated model of probability density prediction is constructed based on the trained probability density prediction model and results, and the weights of different probability density prediction methods are determined, so that the continuous level probability loss of the final integrated prediction model is minimized. This method is finally transformed into a quadratic programming problem, and then the global optimal integration weight is quickly searched by mature commercial software, which improves the accuracy of short-term load forecasting based on probability density and reduces the operating cost of power system dispatching.

Description

technical field [0001] The invention relates to an integration method of multiple prediction results of electric load probability density, belonging to the technical field of power system analysis. Background technique [0002] Power load forecasting is an important part of power system planning and the basis of power system economic operation. In order to assist the power system to make optimal decisions to effectively reduce the planning and operating costs of the power system, high-precision load forecasting is essential. In recent years, with the continuous increase in the scale of the power system, the addition of intermittent energy sources such as wind power and solar energy, and the continuous and rapid growth of distributed renewable energy have made the load of the power system show strong uncertainty. The importance of probabilistic load forecasting research to power system planning and operation has begun to emerge. Compared with traditional point forecasting, ...

Claims

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Application Information

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Patent Type & Authority Patents(China)
IPC IPC(8): G06Q10/04G06Q50/06G06N3/00G06N3/02
CPCG06N3/006G06N3/02G06Q10/04G06Q50/06
Inventor 王毅李天一张宁康重庆
Owner TSINGHUA UNIV
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