Knowledge-Based Segmentation to Improve Accuracy and Explainability in Non-Technical Losses Detection

Albert Calvo, Bernat Coma-Puig, Josep Carmona and Marta Arias
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Albert Calvo: Department of Computer Science, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Bernat Coma-Puig: Department of Computer Science, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Josep Carmona: Department of Computer Science, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain
Marta Arias: Department of Computer Science, Universitat Politècnica de Catalunya, 08034 Barcelona, Spain

Energies, 2020, vol. 13, issue 21, 1-15

Abstract: Utility companies have a great interest in identifying energy losses. Here, we focus on Non-Technical Losses (NTL), which refer to losses caused by utility theft or meter errors. Typically, utility companies resort to machine learning solutions to automate and optimise the identification of such losses. This paper extends an existing NTL-detection framework: by including knowledge-based NTL segmentation, we have detected some opportunities for improving the accuracy and the explanations provided to the utility company. Our improved models focus on specific types of NTL and therefore, the explanations provided are easier to interpret, allowing stakeholders to make more informed decisions. The improvements and results presented in the article may benefit other industrial frameworks.

Keywords: Non-Technical Losses; machine learning; supervised learning; ensemble learning; explainability (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2020
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (1)

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