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The managers of the telecommunication infrastructure face the challenge of detecting and removing anomalies in the area of energy consumption. New technologies such as smart meters present new possibilities for the control and optimization of energy consumption. The aim of the article is to present the framework of a tool for the detection of anomalies related to energy consumption. The developed multi-criteria system for anomaly detection (MSFAD) consists of three methods: time series prediction with Particle Swarm Optimization (PSO), categorization based on absolute energy consumption and segmentation with the use of relative changes in energy consumption. The framework was tested on the energy consumption logs received from a telecommunications company. The analyses show that combining these methods may lead to improved feedback and increase the number of anomalies detected. That, in turn, would allow for a faster response, and increase the quality of the services provided.

Multi-Criteria Decision-Making System for Detecting Anomalies in the Electrical Energy Consumption of Telecommunication Facilities