Load identification method of baseline load model based on Bayesian classification method

A technology of baseline load and identification method, applied in the direction of instruments, data processing applications, forecasting, etc., can solve problems such as power outages, daily management difficulties of power system power supply enterprises, and increase in power grid failure rates, and achieve good data diversity. Effect

Pending Publication Date: 2019-12-13
SHANGHAI MUNICIPAL ELECTRIC POWER CO +2
View PDF5 Cites 5 Cited by
  • Summary
  • Abstract
  • Description
  • Claims
  • Application Information

AI Technical Summary

Problems solved by technology

[0003] With the intensification of the contradiction between power supply and demand and the rapid changes in the power consumption structure of users, the characteristics of power loads in various regions have undergone major changes, such as the continuous and rapid growth of the maximum load, the continuous expansion of the peak-to-valley difference, the continuous increase in the load rate and the annual maximum load utilization hours. The decline and the increasingly sharp contradiction between supply and demand during the peak period have made it increasingly difficult for the power grid to adjust the peak, causing power cuts and staggered peak power consumption to occur frequently across the country, which has led to an increase in the failure rate of the power grid, and at the same time makes electricity users more concerned about the power supply. The reliability of electricity, the quality of electri

Method used

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
View more

Image

Smart Image Click on the blue labels to locate them in the text.
Viewing Examples
Smart Image
  • Load identification method of baseline load model based on Bayesian classification method
  • Load identification method of baseline load model based on Bayesian classification method
  • Load identification method of baseline load model based on Bayesian classification method

Examples

Experimental program
Comparison scheme
Effect test

Embodiment Construction

[0031] 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 part of the embodiments of the present invention, not all of them. Based on the embodiments of the present invention, all other embodiments obtained by persons of ordinary skill in the art without making creative efforts shall fall within the protection scope of the present invention.

[0032] Introduction to the relevant basic principles of the present invention;

[0033] Factors affecting the potential of power distribution peak stagger include:

[0034] (1) Power supply reliability requirement level

[0035] Power supply reliability refers to the ability of the power system to continuously supply power. The requirements for power supply reliability have a relatively large impact on peak shift regulation, so it is an impor...

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

PUM

No PUM Login to view more

Abstract

The invention relates to a load identification method for a baseline load model based on a Bayesian classification method, and the method comprises the following steps: 1, determining whether any twogiven user characteristic quantities are in statistical correlation or not, and eliminating redundant characteristic quantities; 2, forming a training set by the user characteristic quantities which are subjected to judgment and statistics correlation and are subjected to redundant characteristic quantity elimination; 3, calculating a prior probability for each data category in the training set, and further obtaining a corresponding Bayesian probability; 4, calculating the baseline load of the prediction day by combining the Bayesian probability with the power load of the prediction day; and 5, identifying the peak shifting potential load by utilizing the obtained baseline load. Compared with the prior art, the method has the advantages that the single predicted daily baseline load is avoided, the peak shifting potential load cannot be identified, the data diversity is high, and the like.

Description

technical field [0001] The invention relates to the technical field of power grid staggered dispatching, in particular to a load identification method based on a baseline load model of a Bayesian classification method. Background technique [0002] At present, the imbalance between power supply and demand in my country is mainly manifested in structural and periodical power shortages, and the proportion of peak load time is relatively small. Traditional measures such as increasing installed capacity can easily lead to increased grid investment and waste of installed capacity during low-peak load periods. To solve the shortage of peak load supply and periodical power shortage, power companies must do a good job in peak shift scheduling to maximize the effectiveness of limited energy resources. Peak shift dispatching is still a normalized management work to accommodate the gap between supply and demand, control electricity demand, and maintain a stable order of electricity supp...

Claims

the structure of the environmentally friendly knitted fabric provided by the present invention; figure 2 Flow chart of the yarn wrapping machine for environmentally friendly knitted fabrics and storage devices; image 3 Is the parameter map of the yarn covering machine
Login to view more

Application Information

Patent Timeline
no application Login to view more
IPC IPC(8): G06Q10/06G06Q10/04G06Q50/06
CPCG06Q10/067G06Q10/04G06Q50/06
Inventor 庞天宇解梁军郭乃网宋岩沈泉江陈睿杨栋陈开能吴元庆
Owner SHANGHAI MUNICIPAL ELECTRIC POWER CO
Who we serve
  • R&D Engineer
  • R&D Manager
  • IP Professional
Why Eureka
  • Industry Leading Data Capabilities
  • Powerful AI technology
  • Patent DNA Extraction
Social media
Try Eureka
PatSnap group products