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Ultra-short-term prediction method for power load of industrial park factory

A technology for ultra-short-term forecasting and electricity load, applied in forecasting, data processing applications, instruments, etc., can solve problems such as effectiveness discounts, and achieve the effect of improving accuracy

Pending Publication Date: 2021-06-18
STATE GRID CORP OF CHINA +1
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Problems solved by technology

Due to the many factors affecting the power consumption of the factory, the effectiveness of this forecasting method is greatly reduced

Method used

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  • Ultra-short-term prediction method for power load of industrial park factory
  • Ultra-short-term prediction method for power load of industrial park factory
  • Ultra-short-term prediction method for power load of industrial park factory

Examples

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Embodiment 1

[0104] see figure 1 , an ultra-short-term forecasting method for the electricity load of factories in industrial parks, followed by the following steps:

[0105] 1. Obtain the electricity load h of factories in industrial parks through smart meters and other electricity monitoring equipment 1 The historical electricity consumption data of day D, where D is 90, and the electricity consumption monitoring equipment collects electricity consumption every 15 minutes, forming electricity consumption data for 96 time periods every day;

[0106] 2. Use the gray slope correlation analysis to calculate the similarity of historical electricity consumption data, and obtain the similarity ρ of electricity consumption data in each time period between the i-th day and the j-th day h1，i，j :

[0107]

[0108]

[0109]

[0110]

[0111]

[0112]

[0113] In the above formula, M is the total number of time periods per day, is the correlation degree of the historical electri...

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Abstract

An ultra-short-term prediction method for an industrial park factory electrical load comprises the steps of obtaining D-day historical electrical data of the industrial park factory electrical load, performing clustering analysis on the historical electrical data to obtain NCh1-class load electrical data, and then based on existing electrical data of now time periods before a to-be-predicted day, calculating the total correlation degree of the obtained typical power consumption curve of each type of load in the same time period; and then determining the power consumption load prediction basic values of the next npre time periods of the day to be predicted according to the calculation result of the total correlation degree; finally, predicting an error expected value of the same type of daily electricity consumption data by using historical daily electricity consumption data of various loads, and correcting the obtained electricity consumption load prediction basic value to obtain an electricity consumption load prediction result of the next npre time period of the to-be-predicted day. According to the design, the ultra-short-term prediction precision of the power load of the factory is effectively improved.

Description

technical field [0001] The invention belongs to the technical field of distribution network load forecasting, and in particular relates to an ultra-short-term forecasting method for power load of factories in industrial parks based on gray slope correlation degree analysis and OPTICS clustering joint algorithm. Background technique [0002] At present, electricity load forecasting mainly includes two methods. One is the data-driven electricity load forecasting method, which first conducts cluster analysis on the historical data of electricity consumption, and then uses various advanced machine learning algorithms to realize the electricity load forecast. Another method is based on the model-driven power load forecasting method, which generally constructs a power load mechanism model based on the inherent laws of equipment power consumption, and then directly solves the power load based on the forecast parameters of the day to be predicted. [0003] For the ultra-short-term ...

Claims

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Application Information

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Patent Type & Authority Applications(China)
IPC IPC(8): G06Q10/04G06K9/62G06Q50/06
CPCG06Q10/04G06Q50/06G06F18/23
Inventor 侯婷婷方仍存杨东俊张维唐金锐阮博迟赫天
Owner STATE GRID CORP OF CHINA
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