Explainable Artificial Intelligence for Prediction of Non-Technical Losses in Electricity Distribution Networks-Reference-Cited by-同舟云学术

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Explainable Artificial Intelligence for Prediction of Non-Technical Losses in Electricity Distribution Networks

Published:2023 Issue: Volume:11 Page:73104-73115
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Nwafor Obumneme¹^ORCID,Okafor Emmanuel²^ORCID,Aboushady Ahmed A.¹^ORCID,Nwafor Chioma³^ORCID,Zhou Chengke¹^ORCID

Affiliation:

1. School of Computing, Engineering and Built Environment, Glasgow Caledonian University, Glasgow, Scotland, U.K.

2. SDAIA-KFUPM Joint Research Center for Artificial Intelligence, King Fahd University of Petroleum and Minerals, Dhahran, Saudi Arabia

3. Glasgow School for Business and Society, Glasgow Caledonian University, Glasgow, Scotland, U.K.

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/10005208/10183983.pdf?arnumber=10183983

Reference40 articles.

1. Ensemble Methods in Machine Learning

2. Imbalanced learning algorithm based intelligent abnormal electricity consumption detection

3. Beyond short snippets: Deep networks for video classification;ng;Proc IEEE Conf Comput Vis Pattern Recognit (CVPR),2015

4. Energy Theft Detection Using Gradient Boosting Theft Detector With Feature Engineering-Based Preprocessing

Cited by 1 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Reducing non-technical losses in electricity distribution networks: Leveraging explainable AI and three lines of defence model to manage operational staff-related factors;e-Prime - Advances in Electrical Engineering, Electronics and Energy;2024-09

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