Comparison of supervised learning techniques for non-technical loss detection in power utility
Non technical losses (NTLs) originating from electricity theft and other customer malfeasances are a problem in the electricity supply industry. In recent times, electricity consumer dishonesty has become a universal problem faced by all power utilities. Previous work carried out for NTL detection r...
Saved in:
Main Authors: | Yap, K.S., Tiong, S.K., Nagi, J., Koh, J.S.P., Nagi, F. |
---|---|
Format: | Article |
Language: | en_US |
Published: |
2017
|
Tags: |
Add Tag
No Tags, Be the first to tag this record!
|
Similar Items
-
Improving SVM-based nontechnical loss detection in power utility using the fuzzy inference system
by: Nagi, J., et al.
Published: (2017) -
Improving SVM-based nontechnical loss detection in power utility using the fuzzy inference system
by: Nagi J., et al.
Published: (2023) -
Non-technical loss analysis for detection of electricity theft using support vector machines
by: Nagi, J., et al.
Published: (2017) -
Nontechnical loss detection for metered customers in power utility using support vector machines
by: Nagi, J., et al.
Published: (2017) -
Nontechnical loss detection for metered customers in power utility using support vector machines
by: Nagi J., et al.
Published: (2023)