Non-technical losses detection in energy consumption focusing on energy recovery and explainability
Non-technical losses (NTL) is a problem that many utility companies try to solve, often using black-box supervised classification algorithms. In general, this approach achieves good results. However, in practice, NTL detection faces technical, economic, and transparency challenges that cannot be eas...
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| Published in | Machine learning Vol. 111; no. 2; pp. 487 - 517 |
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| Main Authors | , |
| Format | Journal Article |
| Language | English |
| Published |
New York
Springer US
01.02.2022
Springer Nature B.V |
| Subjects | |
| Online Access | Get full text |
| ISSN | 0885-6125 1573-0565 |
| DOI | 10.1007/s10994-021-06051-1 |
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| Abstract | Non-technical losses (NTL) is a problem that many utility companies try to solve, often using black-box supervised classification algorithms. In general, this approach achieves good results. However, in practice, NTL detection faces technical, economic, and transparency challenges that cannot be easily solved and which compromise the quality and fairness of the predictions. In this work, we contextualise these problems in an NTL detection system built for an international utility company. We explain how we have mitigated them by moving from classification into a regression system and introducing explanatory techniques to improve its accuracy and understanding. As we show in this work, the regression approach can be a good option to mitigate these technical problems, and can be adjusted in order to capture the most striking NTL cases. Moreover, explainable AI (through Shapley Values) allows us to both validate the correctness of the regression approach in this context beyond benchmarking, and improve the transparency of our system drastically. |
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| AbstractList | Non-technical losses (NTL) is a problem that many utility companies try to solve, often using black-box supervised classification algorithms. In general, this approach achieves good results. However, in practice, NTL detection faces technical, economic, and transparency challenges that cannot be easily solved and which compromise the quality and fairness of the predictions. In this work, we contextualise these problems in an NTL detection system built for an international utility company. We explain how we have mitigated them by moving from classification into a regression system and introducing explanatory techniques to improve its accuracy and understanding. As we show in this work, the regression approach can be a good option to mitigate these technical problems, and can be adjusted in order to capture the most striking NTL cases. Moreover, explainable AI (through Shapley Values) allows us to both validate the correctness of the regression approach in this context beyond benchmarking, and improve the transparency of our system drastically. |
| Author | Coma-Puig, Bernat Carmona, Josep |
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| CitedBy_id | crossref_primary_10_1109_ACCESS_2023_3295688 crossref_primary_10_1109_ACCESS_2025_3541722 crossref_primary_10_1007_s41403_022_00362_y crossref_primary_10_1016_j_jclepro_2024_142655 crossref_primary_10_1016_j_engappai_2025_110363 crossref_primary_10_3390_en16217460 crossref_primary_10_1016_j_engappai_2023_105904 crossref_primary_10_1109_ACCESS_2024_3426940 |
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| Keywords | Regression Robustness Non-technical losses Explainability Classification |
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| SubjectTerms | Algorithms Artificial Intelligence Classification Coma Computer Science Control Customers Energy consumption Energy industry Energy recovery Machine Learning Mechatronics Natural Language Processing (NLP) Neural networks Public utilities Regression Robotics Simulation and Modeling Special Issue: Foundations of Data Science Support vector machines |
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