Analyzing seasonal variation in residential load patterns via two-stage clustering and relative entropy: poster

Zhenyu Wang, Hao Wang

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

2 Citations (Scopus)


Residential load data generated by smart meters usually contain valuable information on household electricity consumption behaviors. Identifying load patterns and establishing typical consumer profiles will benefit stakeholders, including consumers, retailers, and operators. Load clustering has been widely studied in consumption segmentation and load profiling. However, it appears that the same consumers may vary their daily load patterns when the season changes, calling for comprehensive analysis into seasonal load patterns. This paper aims to study seasonal variations in load patterns. We model the seasonal load patterns using two-stage K-Medoids clustering and evaluate the relative entropy of the cluster distributions between seasons. Based on real-world load data, we find that 32% consumers hold similar load patterns in different seasons, whereas 14% consumers tend to vary their consumption habits when certain seasons come.

Original languageEnglish
Title of host publicationProceedings of the 2021 The Twelfth ACM International Conference on Future Energy Systems
EditorsYashar Ghiassi-Farrokhfal
Place of PublicationNew York NY USA
PublisherAssociation for Computing Machinery (ACM)
Number of pages2
ISBN (Electronic)9781450383332
Publication statusPublished - 2021
EventACM International Conference on Future Energy Systems 2021 - Online, Italy
Duration: 28 Jun 20212 Jul 2021
Conference number: 12th (Proceedings)


ConferenceACM International Conference on Future Energy Systems 2021
Abbreviated titlee-Energy 2021
Internet address


  • clustering
  • relative Entropy
  • residential electricity load
  • Smart meter

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