Abstract
| Original language | English |
|---|---|
| Title of host publication | Proceedings of the 42nd Universities Power Engineering Conference |
| Publisher | IEEE |
| ISBN (Electronic) | 9781905593347 |
| ISBN (Print) | 9781905593361, 9781905593330 |
| DOIs | |
| Publication status | Published - 2007 |
| Event | 42nd International Universities Power Engineering Conference - University of Brighton, Brighton, United Kingdom Duration: 4 Sep 2007 → 6 Sep 2007 Conference number: 42 |
Conference
| Conference | 42nd International Universities Power Engineering Conference |
|---|---|
| Abbreviated title | UPEC 2007 |
| Country | United Kingdom |
| City | Brighton |
| Period | 4/09/07 → 6/09/07 |
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A review of electricity load profile classification methods. / Prahastono, Iswan; King, David J.; Ozveren, Cuneyt Suheyl.
Proceedings of the 42nd Universities Power Engineering Conference. IEEE , 2007.Research output: Chapter in Book/Report/Conference proceeding › Conference contribution
TY - GEN
T1 - A review of electricity load profile classification methods
AU - Prahastono, Iswan
AU - King, David J.
AU - Ozveren, Cuneyt Suheyl
PY - 2007
Y1 - 2007
N2 - 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.
AB - 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.
U2 - 10.1109/UPEC.2007.4469120
DO - 10.1109/UPEC.2007.4469120
M3 - Conference contribution
SN - 9781905593361
SN - 9781905593330
BT - Proceedings of the 42nd Universities Power Engineering Conference
PB - IEEE
ER -