Smart grid

Electrical engineering

Physics

Accounting

Business

Pure mathematics

Smart meter

Mathematics

Data mining

Astronomy

Machine learning

Engineering

Supervised learning

Metre

Artificial intelligence

Power (physics)

Revenue

Electricity

Computer science

Quantum mechanics

Field (mathematics)

Artificial neural network

Electricity meter

IEEE Transactions on Smart Grid

Non-technical electricity losses due to anomalies or frauds are accountable for important revenue losses in power utilities. Recent advances have been made in this area, fostered by the roll-out of smart meters. In this paper, we propose a methodology for non-technical loss detection using supervised learning. The methodology has been developed and tested on real smart meter data of all the industrial and commercial customers of Endesa. This methodology uses all the information the smart meters record (energy consumption, alarms and electrical magnitudes) to obtain an in-depth analysis of the customer's consumption behavior. It also uses auxiliary databases to provide additional information regarding the geographical location and technological characteristics of each smart meter. The model has been trained, validated and tested on the results of approximately 57000 on-field inspections. It is currently in use in a non-technical loss detection campaign for big customers. Several state-of-theart classifiers have been tested. The results show that extreme gradient boosted trees outperform the rest of the classifiers.

Detection of Non-Technical Losses Using Smart Meter Data and Supervised Learning