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A fast CVR electric load forecast method for large samples

A prediction method and power load technology, which can be applied to computational models, electrical components, circuit devices, etc., can solve problems such as large space-time overhead, and achieve the effect of expanding space-time overhead, reducing time and space complexity, and improving training speed.

Inactive Publication Date: 2009-10-07
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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Problems solved by technology

[0015] The object of the present invention is to propose a large-scale sample-oriented CVR fast prediction method for electric loads to reduce the time and space expenses of short-term electric load forecasting and improve Prediction accuracy

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  • A fast CVR electric load forecast method for large samples
  • A fast CVR electric load forecast method for large samples
  • A fast CVR electric load forecast method for large samples

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Embodiment Construction

[0044] The preferred embodiments will be described in detail below with reference to the accompanying drawings. It should be emphasized that the following description is exemplary only, and is not intended to limit the scope of the invention and its application.

[0045] figure 1 This is the overall flow chart of the large-scale sample-oriented CVR power load rapid prediction method provided by this method. figure 1 , the method of the present invention comprises the steps:

[0046] The first step, data analysis and processing, includes the following steps:

[0047] (1) Preprocess the measured historical data. According to some statistical characteristics of the data, some possible abnormal points of the original data can be found and eliminated or corrected. For the processing of data defects, if there is a large amount of distorted data or no data in the data of a certain day, the load data of the same date type is used as the filling principle and the historical data of...

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Abstract

The present invention discloses a fast CVR electric load forecast method for large samples in the technical field of short-term electric load forecast technology. The technical scheme: firstly, make up the incomplete historical data and check and smooth the data by horizontal check method and vertical check method; secondly, reorganize the data of temperature and load sample sets by time flow; thirdly, quickly exercise the above two large sample data through two CVR; lastly, make continuous rolling forecast on the temperature information in future time slot by using a VCR until the temperature values of all forecast points are obtained, and then use the result and the other VCR to make continuous rolling forecast of load until the forecast values of all forecast points are obtained. Under super large samples, the present invention raises forecast speed, guarantees forecast accuracy and effectively supports the accurate and fast forecast of super large samples of electric load.

Description

technical field [0001] The invention belongs to the technical field of short-term power load forecasting, and in particular relates to a large-scale sample-oriented CVR power load fast forecasting method. Background technique [0002] Short-term power load forecasting technology can be used to predict the load change trend of the power system in the next few hours to days. It is the most important link to ensure the safe and economical operation of the entire power system, and is also an important basis for formulating power generation plans and power flow calculations. At present, the knowledge-based self-learning method mostly uses a part of the original data as the training sample data in the application of power load forecasting to prevent the problem that the training time is too long due to the large sample data set. Local prediction, and the accuracy is reduced due to incomplete samples, and it is impossible or difficult to solve the prediction needs of special cases...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/00G06Q50/00G06N1/00H02J3/00G06N99/00G06Q10/08
CPCY04S10/54Y04S10/50
Inventor 李元诚刘克文
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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