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Electricity customer short-term load demand forecasting method and device

A demand forecasting and short-term load technology, which is applied in the field of big data processing, can solve the problems that cannot meet the requirements of power load forecasting speed and accuracy, and achieve the effect of meeting the requirements of speed and forecasting accuracy

Inactive Publication Date: 2016-08-10
BEIJING CHINA POWER INFORMATION TECH +3
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  • Abstract
  • Description
  • Claims
  • Application Information

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Problems solved by technology

[0012] However, the inventors found that the above-mentioned method is suitable for power consumption forecasting with a small amount of data, and cannot meet the requirements for the speed and accuracy of power load forecasting under the current massive load information

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  • Electricity customer short-term load demand forecasting method and device
  • Electricity customer short-term load demand forecasting method and device
  • Electricity customer short-term load demand forecasting method and device

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Embodiment Construction

[0058] The following will clearly and completely describe the technical solutions in the embodiments of the present invention with reference to the accompanying drawings in the embodiments of the present invention. Obviously, the described embodiments are only some, not all, embodiments of the present invention. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts belong to the protection scope of the present invention.

[0059] Such as figure 1 As shown, a method for forecasting short-term load demand of electricity consumers provided by Embodiment 1 of the present invention includes:

[0060] S1: Cluster the collected historical daily load curve data of each electricity customer according to date;

[0061] S2: Establish a load forecasting model at each time point for the date group obtained by clustering;

[0062] S3: Find a historical similar day that matches the date to b...

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Abstract

The invention provides an electricity customer short-term load demand forecasting method. The method comprises the following steps: carrying out clustering on collected historical daily load curve data of electricity customers according to dates; then, establishing a load forecasting model at each time for each date group obtained through clustering; and finally, searching a historical similar day matched with a date to be forecasted, and obtaining a load forecasting result of the date to be forecasted according to the load forecasting model of the date group where the historical similar day belongs. According to the scheme, a parallel computation framework is utilized, and electricity load demands of mass electricity customers can be forecasted simultaneously; and requirements of mass data analysis speed and prediction accuracy are met.

Description

technical field [0001] The invention relates to the technical field of big data processing, in particular to a method and device for predicting short-term load demand of electricity consumers. Background technique [0002] With the continuous increase of electricity consumption information and the continuous improvement of customer basic information, the power information data shows an explosive growth trend. Load forecasting is a key link in power grid planning and an important calculation basis for substation and grid planning. High-precision short-term load forecasting can effectively predict the load demand and load changes of electricity customers in the future, which is very important for improving service quality and peak load reduction. Valley filling and ensuring the smooth operation of the power grid play a key role. [0003] At present, the commonly used electricity demand forecasting methods are as follows: [0004] 1. Time series method [0005] Time series a...

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06Q50/06
CPCG06Q10/04G06Q50/06
Inventor 刘建赵加奎李宏发黄秋岑刘芳刘玉玺方红旺欧阳红郝庆利卢耀宗程华福
Owner BEIJING CHINA POWER INFORMATION TECH