Method for identifying pattern of load cycle

Patent metadata

Field	Value
Publication number	US-11043808-B2
Application number	US-201715778312-A
Country	US
Kind code	B2
Filing date	Oct 30, 2017
Priority date	Nov 2, 2016
Publication date	Jun 22, 2021
Grant date	Jun 22, 2021

Abstract

A method for identifying a pattern of a load cycle includes: performing statistics on a daily load of a system based on smart meter data; generating a curve of the daily load of the system according to the statistics on the daily load of the system; acquiring a result of clustering curves of loads of typical days by applying shape-based time sequence clustering analysis using the curve of the daily load of the system; and identifying a pattern of a load cycle according to the result of clustering the curves of the loads of the typical days.

First claim

The invention claimed is:

1. A method for identifying a pattern of a load cycle, comprising:
performing statistics on a daily load of a system based on smart meter data;
generating a curve of the daily load of the system according to the statistics on the daily load of the system;
acquiring a result of clustering curves of loads of typical days by applying shape-based time sequence clustering analysis using the curve of the daily load of the system; and
identifying a pattern of a load cycle according to the result of clustering the curves of the loads of the typical days;
performing load prediction according to the result of clustering the curves of the loads of the typical days,
wherein the generating a curve of the daily load of the system comprises:
acquiring a curve of a load for 24 hours of the system by accumulating a curve of a load for 24 hours consumed by each smart meter user in an area or the system,
wherein the smart meter data comprise active power, reactive power, a voltage, a current, and a power factor, wherein the load is an active power reading,
wherein the curve of the daily load of the system describes variation of the load over time within a day, wherein the curve of the daily load varies depending on a workday, a weekend, or a holiday of a season in a region, wherein a curve of a load of a typical day in a typical season, comprising a curve of a load of a typical day and a typical curve of a continued daily load, is used,
wherein the performing load prediction according to the result of clustering the curves of the loads of the typical days comprises: searching for a similar day in history according to a factor, grouping or clustering by the shape-based time sequence clustering analysis, and a curve of the daily load in a historical year, and estimating a curve of a load of the system for a day to be predicted according to a curve of the load for the similar day in history, a curve of the load for recent days, and weather forecast data,
wherein the factor comprises at least one of a type of a date, a period of time for central heating, a temperature, or a rainfall.

2. The method according to claim 1 , wherein time sequence clustering analysis depends on measurement of a distance between a data point and a prototype, wherein curves of similar shapes are clustered together by shape-based clustering, to reduce impact of a difference in an amplitude and a difference in a phase on time sequence clustering,
wherein a similarity between shapes of two time sequences is measured via shape-based time sequence clustering analysis by computing cross-correlation of the two time sequences, by
comparing the similarity between a time sequence =(x 1 , . . . , x m ) and a time sequence =(y 1 , . . . , y m ), by first keeping the time sequence invariant and computing a distance by which the time sequence is to be translated as:

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Assignees

• China Electric Power Res Institute Company Limited
• State Grid Corp China
• State Grid Fujian Electric Power Res Institute
• Global Energy Interconnection Res Inst Co Ltd
• China Electric Power Res Inst Co Ltd
• State Grid Fujian Electric Power Res Inst

Inventors

• Chen Zhenyu
• Li Dapeng
• Huang Yunhao
• Di Fangchun
• Li Lixin
• Liu Guangyi
• Zhu Wendong
• Hao Yangchun
• Zhang Yi
• Chen Jinxiang

Classifications

• H02J3/00 Primary

  Circuit arrangements for AC mains or AC distribution networks · CPC title

• H02J3/003

  Load forecast, e.g. methods or systems for forecasting future load demand · CPC title

• G06Q10/04 Primary

  Forecasting or optimisation specially adapted for administrative or management purposes, e.g. linear programming or "cutting stock problem" (market predictions or forecasting for commercial activities G06Q30/0202) · CPC title

• G05B13/048

  using a predictor · CPC title

• Y04S10/50

  Systems or methods supporting the power network operation or management, involving a certain degree of interaction with the load-side end user applications · CPC title