TY - GEN
T1 - Load profiling method in detecting non-technical loss activities in a power utility
AU - Nizar, A. H.
AU - Dong, Z. Y.
AU - Jalaluddin, M.
AU - Raffles, M. J.
N1 - Publication details (e.g. title, author(s), publication statuses and dates) are captured on an “AS IS” and “AS AVAILABLE” basis at the time of record harvesting from the data source. Suggestions for further amendments or supplementary information can be sent to [email protected].
PY - 2006
Y1 - 2006
N2 - This paper presents comprehensive review on non-technical losses, load profiles and data mining techniques that currently being used in effort to minimize the non-technical loss activities. It also presents on the contributing factors in load profiles of electricity customers, using the knowledge discovery in databases (KDD) procedure, to determine the load profiles for different types of customers. In this paper, the customer load profiles are compared based on the type of day, by analysing their differences in their consumption behaviour. The objective of this study is to use the load profiling methods and data mining techniques to classify, detect and predict non-technical losses in the distribution sector, due to faulty metering and billing errors, as well as to gather knowledge on customer behaviour and preferences so as to gain a competitive advantage in the electricity market. This paper focuses mainly on the development of framework analysis of the customer behaviour. © 2006 IEEE.
AB - This paper presents comprehensive review on non-technical losses, load profiles and data mining techniques that currently being used in effort to minimize the non-technical loss activities. It also presents on the contributing factors in load profiles of electricity customers, using the knowledge discovery in databases (KDD) procedure, to determine the load profiles for different types of customers. In this paper, the customer load profiles are compared based on the type of day, by analysing their differences in their consumption behaviour. The objective of this study is to use the load profiling methods and data mining techniques to classify, detect and predict non-technical losses in the distribution sector, due to faulty metering and billing errors, as well as to gather knowledge on customer behaviour and preferences so as to gain a competitive advantage in the electricity market. This paper focuses mainly on the development of framework analysis of the customer behaviour. © 2006 IEEE.
KW - Classification
KW - Clustering
KW - Customer information system
KW - Data mining
KW - Deregulation
KW - Load profiles
KW - Non-technical losses (NTL)
UR - https://www.scopus.com/pages/publications/46249095463
UR - https://www.scopus.com/record/pubmetrics.uri?eid=2-s2.0-46249095463&origin=recordpage
U2 - 10.1109/PECON.2006.346624
DO - 10.1109/PECON.2006.346624
M3 - RGC 32 - Refereed conference paper (with host publication)
SN - 1424402735
SN - 9781424402731
T3 - First International Power and Energy Conference, (PECon 2006) Proceedings
SP - 82
EP - 87
BT - First International Power and Energy Conference, (PECon 2006) Proceedings
T2 - 1st International Power and Energy Conference, PECon 2006
Y2 - 28 November 2006 through 29 November 2006
ER -