| Autor: |
Obumneme Nwafor, Chioma Nwafor, Ahmed Aboushady, Ahmed Solyman |
| Jazyk: |
angličtina |
| Rok vydání: |
2024 |
| Předmět: |
|
| Zdroj: |
e-Prime: Advances in Electrical Engineering, Electronics and Energy, Vol 9, Iss , Pp 100748- (2024) |
| Druh dokumentu: |
article |
| ISSN: |
2772-6711 |
| DOI: |
10.1016/j.prime.2024.100748 |
| Popis: |
This study presents a multidisciplinary approach involving Explainable Artificial Intelligence (ExAI) and operational risk management to reduce Non-Technical Losses (NTL) in electricity distribution. It empirically explores how the activities of employees of utility companies contribute to NTL, a phenomenon often overlooked in existing empirical research. An ensemble classification algorithm is used to analyse utility operations data, and the SHAP explainability technique establishes the predictive significance of staff activities for NTL. Subsequently, these staff activities are mapped into risk cells using the BASEL II and III operational risk definitions, and the Three Lines of Defence (3LoD) model is developed for optimizing electricity distribution. The paper makes three original contributions to the literature: first, it empirically links staff operations to NTL; second, it maps NTL causes to Basel II/III operational risk categories; and finally, to the best of the authors’ knowledge, it is the first study to use the 3LoD model for electricity distribution optimization. |
| Databáze: |
Directory of Open Access Journals |
| Externí odkaz: |
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