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A Short-Term Power Load Forecasting Method

A short-term power load and forecasting method technology, applied in the direction of electrical digital data processing, special data processing applications, instruments, etc., to achieve strong generalization ability and fast forecasting process

Inactive Publication Date: 2011-12-07
NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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  • Summary
  • Abstract
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  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0007] In view of the shortcomings of the single forecasting method mentioned in the above background technology in terms of forecasting accuracy and training speed, the present invention proposes a short-term power load forecasting method

Method used

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  • A Short-Term Power Load Forecasting Method

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Embodiment Construction

[0029] The preferred embodiments will be described in detail below in conjunction with the accompanying drawings. It should be emphasized that the following description is only exemplary and not intended to limit the scope of the invention and its application.

[0030] figure 1 is a flowchart of the present invention. figure 1 Among them, the method provided by the invention comprises the following steps:

[0031] Step 1: Construct a sample set through the load data of the data acquisition and monitoring control system;

[0032] Step 1.1: Take out the load data of the first 20 days of the same type as the forecast day in the data acquisition and monitoring control system (Supervisory Control And DataAcquisition, SCADA);

[0033] Step 1.2: Statistics related data, select input volume;

[0034] Through experiments, it is found that in order to reduce the training time, unnecessary sample dimensions need to be reduced. These inputs include: daily maximum temperature, minimum ...

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Abstract

The invention discloses a short-term power load forecasting method in the technical field of power load forecasting. The present invention constructs a sample set through the load data of the data collection and monitoring control system, and denoises the sample set through curvelet transform to obtain a denoised sample set; divides the denoised sample set into a test set and a training set; Use the training set and learning machine to generate multiple training models, and then use the bagging algorithm to obtain the final prediction model; finally use the final prediction model and test set to predict the load. The invention not only solves the problems of small amount of sample data, large deviation and uncertainty, but also has stronger generalization ability than a single learning machine, can effectively integrate multiple models, and makes the prediction process more rapid and accurate.

Description

technical field [0001] The invention belongs to the technical field of electric load forecasting, and in particular relates to a short-term electric load forecasting method. Background technique [0002] Short-term power load forecasting is an important part of power load forecasting and the basis for formulating economical and reasonable power supply plans. Improving the level of load forecasting technology is conducive to rationally arranging the operation mode of the power grid and the maintenance plan of the unit, which is conducive to saving coal, fuel and reducing power generation costs, thereby improving the economic and social benefits of the power system. [0003] The biggest characteristic of short-term load forecasting is its obvious periodicity. Specifically include: the similarity of the 24-hour overall load change law between different days; the similarity of different weeks and the same week type days; the similarity of working days and rest days; the similar...

Claims

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Application Information

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IPC IPC(8): G06F19/00
Inventor 李元诚王旭峰
Owner NORTH CHINA ELECTRIC POWER UNIV (BAODING)
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