Work

Data-Driven Anomaly Detection for Smart Energy Meters

Year: 2024

Type: conference-paper

Source: IEEE International Workshop on Applied Measurements for Power Systems (AMPS)

DOI: 10.1109/amps62611.2024.10706664

Authors: Sina Ronaghi, Alessandro Ferrero, Simona Salicone, Harsha Vardhana Jetti

Institutions: Politecnico di Milano, University of Perugia

Language: English

Topic: Electricity Theft Detection Techniques

Subfield: Electrical and Electronic Engineering

Field: Engineering

Domain: Physical Sciences

SDGs: Industry, innovation and infrastructure, Affordable and clean energy

Open Access status: closed

OpenAlex ID: W4403278417

FWCI: 1.612

Cites: 21

Cited by: 2

Related to: 10