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Please use this identifier to cite or link to this item: http://hdl.handle.net/10373/748

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Title: A review of Electricity Load Profile Classification methods
Authors: Prahastono, Iswan
King, David J.
Özveren, Cüneyt Süheyl
Affiliation: University of Abertay Dundee. School of Computing & Engineering Systems
Keywords: Electricity load profile classification
Clustering methods
Hierarchical
K-means
Follow the leader
Fuzzy K-means
Fuzzy classification
Issue Date: 2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Type: Conference Paper
Refereed: peer-reviewed
Rights: This is the author's final version of this conference paper. Published version (c)IEEE, available from http://dx.doi.org/10.1109/UPEC.2007.4469120
Citation: Prahastono, I., King, D.J. and Ozveren, C.S. 2007. A review of Electricity Load Profile Classification methods. In: Proceedings of the 42nd Universities Power Engineering Conference, Brighton, 4-6 September 2007. pp. 1187-1191. Available from http://dx.doi.org/10.1109/UPEC.2007.4469120
Abstract: 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.
URI: http://hdl.handle.net/10373/748
ISBN: 9781905593361
Appears in Collections:Computing & Engineering Systems Collection

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