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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
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
Non-Technical Loss (NTL),
Non-Technical Loss detection
machine learning
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
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