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001-es BibID:BIBFORM108643
035-os BibID:(Scopus)85107891435
Első szerző:Ghori, Khawaja Moyeezullah (informatikus)
Cím:A Review on Latest Trends in Non-Technical Loss Detection / Khawaja MoyeezUllah Ghori, Muhammad Awais, Akmal Saeed Khattak, Muhammad Imran, Rabeeh Ayaz Abbasi, Laszlo Szathmary
Dátum:2021
ISSN:16130073
Megjegyzések:An increasing interest in digging out the consumption patterns in power and energy sector is observed globally. This includes electrical, gas, and water supply industries. A reason behind analyzing the consumption patterns is the detection of fraudulent attempts which are made for the illegal reduction of bill payments. In the case of electricity, these attempts are made by reversing the meters, by-passing or slowing down the meters or inaccurate readings. The detection of theft attempts in power industry is termed as Non-Technical Loss (NTL) detection. With the increasing demand for electricity, the occurrences of NTL have been reported globally including India, Pakistan, Brazil and China etc. In this paper, we first describe the use of the synthesized and the real datasets in NTL detection. Then, we highlight an interesting characteristic of class imbalance that is exhibited in the datasets used for NTL detection. Moreover, we identify the fruitful areas in NTL detection where the research community has been working on. Lastly, we discuss the need for a relative comparison of the classical machine learning and deep learning over a benchmark dataset for NTL detection. Keywords: Non-Technical Loss (NTL), Non-Technical Loss detection, machine learning, classification, class imbalance.
Tárgyszavak:Műszaki tudományok Informatikai tudományok előadáskivonat
könyvrészlet
Non-Technical Loss (NTL),
Non-Technical Loss detection
machine learning
classification
class imbalance
Megjelenés:CEUR Workshop Proceedings / szerk. Fazekas István, Hajdu András, Tómács Tibor. - 2874 (2021), p. 131-139. -
További szerzők:Awais, Muhammad Akmal Saeed Khattak Imran, Muhammad (1981-) (informatikus) Abbasi, Rabeeh Ayaz Szathmáry László (1977-) (programtervező-informatikus)
Pályázati támogatás:EFOP-3.6.1-16-2016-00022
EFOP
Internet cím:Szerző által megadott URL
Intézményi repozitóriumban (DEA) tárolt változat
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2.

001-es BibID:BIBFORM082862
035-os BibID:(Scopus)85083522586
Első szerző:Ghori, Khawaja Moyeezullah (informatikus)
Cím:Impact of Feature Selection on Non-Technical Loss Detection / Khawaja MoyeezUllah Ghori, Rabeeh Ayaz Abbasi, Muhammad Awais, Muhammad Imran, Ata Ullah, Laszlo Szathmary
Dátum:2020
ISBN:978-1-7281-2746-0
Tárgyszavak:Természettudományok Matematika- és számítástudományok előadáskivonat
könyvrészlet
Megjelenés:6th Conference on Data Science and Machine Learning Applications. - p. 19-24. -
További szerzők:Abbasi, Rabeeh Ayaz Awais, Muhammad Imran, Muhammad (1981-) (informatikus) Ullah, Ata Szathmáry László (1977-) (programtervező-informatikus)
Internet cím:Intézményi repozitóriumban (DEA) tárolt változat
DOI
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3.

001-es BibID:BIBFORM082861
035-os BibID:(WoS)000524745100014 (Scopus)85077292469
Első szerző:Ghori, Khawaja Moyeezullah (informatikus)
Cím:Performance Analysis of Different Types of Machine Learning Classifiers for Non-Technical Loss Detection / Khawaja Moyeezullah Ghori, Rabeeh Ayaz Abbasi, Muhammad Awais, Muhammad Imran, Ata Ullah, Laszlo Szathmary
Dátum:2020
ISSN:2169-3536
Megjegyzések:With the ever-growing demand of electric power, it is quite challenging to detect and prevent Non-Technical Loss (NTL) in power industries. NTL is committed by meter bypassing, hooking from the main lines, reversing and tampering the meters. Manual on-site checking and reporting of NTL remains an unattractive strategy due to the required manpower and associated cost. The use of machine learning classifiers has been an attractive option for NTL detection. It enhances data-oriented analysis and high hit ratio along with less cost and manpower requirements. However, there is still a need to explore the results across multiple types of classifiers on a real-world dataset. This paper considers a real dataset from a power supply company in Pakistan to identify NTL. We have evaluated 15 existing machine learning classifiers across 9 types which also include the recently developed CatBoost, LGBoost and XGBoost classifiers. Our work is validated using extensive simulations. Results elucidate that ensemble methods and Artificial Neural Network (ANN) outperform the other types of classifiers for NTL detection in our real dataset. Moreover, we have also derived a procedure to identify the top-14 features out of a total of 71 features, which are contributing 77% in predicting NTL. We conclude that including more features beyond this threshold does not improve performance and thus limiting to the selected feature set reduces the computation time required by the classifiers. Last but not least, the paper also analyzes the results of the classifiers with respect to their types, which has opened a new area of research in NTL detection.
Tárgyszavak:Természettudományok Informatikai tudományok idegen nyelvű folyóiratközlemény külföldi lapban
folyóiratcikk
Megjelenés:IEEE Access. - 8 (2020), p. 16033-16048. -
További szerzők:Abbasi, Rabeeh Ayaz Awais, Muhammad Imran, Muhammad (1981-) (informatikus) Ullah, Ata Szathmáry László (1977-) (programtervező-informatikus)
Pályázati támogatás:EFOP-3.6.3-VEKOP-16-2017-00002
EFOP
Internet cím:Intézményi repozitóriumban (DEA) tárolt változat
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