A review of electricity load profile classification methods

Iswan Prahastono, David J. King, Cuneyt Suheyl Ozveren

Research output: Chapter in Book/Report/Conference proceedingConference contribution

23 Citations (Scopus)
141 Downloads (Pure)


With the electricity market liberalisation in Indonesia, the electricity companies will have the right to develop tariff rates independently. Thus, precise knowledge of load profile classifications of customers will become essential for designing a variety of tariff options, in which the tariff rates are in line with efficient revenue generation and will encourage optimum take up of the available electricity supplies, by various types of customers. Since the early days of the liberalisation of the Electricity Supply Industries (ESI) considerable efforts have been made to investigate methodologies to form optimal tariffs based on customer classes, derived from various clustering and classification techniques. Clustering techniques are analytical processes which are used to develop groups (classes) of customers based on their behaviour and to derive representative sets of load profiles and help build models for daily load shapes. Whereas classification techniques are processes that start by analysing load demand data (LDD) from various customers and then identify the groups that these customers' LDD fall into. In this paper we will review some of the popular clustering algorithms, explain the difference between each method.
Original languageEnglish
Title of host publicationProceedings of the 42nd Universities Power Engineering Conference
ISBN (Electronic)9781905593347
ISBN (Print)9781905593361, 9781905593330
Publication statusPublished - 2007
Event42nd International Universities Power Engineering Conference - University of Brighton, Brighton, United Kingdom
Duration: 4 Sep 20076 Sep 2007
Conference number: 42


Conference42nd International Universities Power Engineering Conference
Abbreviated titleUPEC 2007
Country/TerritoryUnited Kingdom


  • Electricity load profile classification
  • Clustering methods
  • Hierarchical
  • Follow the leader
  • Fuzzy classification
  • K-means
  • Fuzzy K-means


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