Skip to main navigation Skip to search Skip to main content

FCM based Demand Response Baseline Load Estimation Using Smart Meter Data

  • Xi'an Jiaotong University

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

2 Scopus citations

Abstract

Customer baseline load estimation is the basis of Demand Response(DR) for it decides the DR performance evaluation and financial settlement of customer participation. The accurate estimation of baseline load is hard because of complexity and uncertainty of customer behavior. Besides, baseline load is an ideal load which cannot be metered in reality. This paper proposed a data driven Fuzzy C Means(FCM) based baseline load estimation method. The fuzzy characteristics of customer electricity consumption behavior is adequately considered and the complex factors of customer behavior are implicitly considered. The proposed method has high accuracy compared to conventional methods.

Original languageEnglish
Title of host publicationProceedings of 2021 IEEE 4th International Electrical and Energy Conference, CIEEC 2021
PublisherInstitute of Electrical and Electronics Engineers Inc.
ISBN (Electronic)9781728171494
DOIs
StatePublished - 28 May 2021
Event4th IEEE China International Electrical and Energy Conference, CIEEC 2021 - Wuhan, China
Duration: 28 May 202130 May 2021

Publication series

NameProceedings of 2021 IEEE 4th International Electrical and Energy Conference, CIEEC 2021

Conference

Conference4th IEEE China International Electrical and Energy Conference, CIEEC 2021
Country/TerritoryChina
CityWuhan
Period28/05/2130/05/21

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

  1. SDG 7 - Affordable and Clean Energy
    SDG 7 Affordable and Clean Energy

Keywords

  • baseline load estimation
  • data driven
  • demand response
  • fuzzy c means
  • machine learning

Fingerprint

Dive into the research topics of 'FCM based Demand Response Baseline Load Estimation Using Smart Meter Data'. Together they form a unique fingerprint.

Cite this