IRIT and  LMDC , Toulouse University


Anomaly detection, multi-agent system, smart buildings, energy management, data stream

This research project deals with energy efficiency in buildings to mitigate the climate change. Buildings are the highest source of energy consumption worldwide. However, a large part of this energy is wasted, mainly due to poor buildings management. Therefore, being accurately informed about consumptions and detecting anomalies are essential steps to overcome this problem. Currently, some existing software can record, store, archive, and visualize big data such as the ones of a building, a campus, or a city. Yet, they do not provide Artificial Intelligence (AI) able to automatically analyze the streaming data to detect anomalies and send alerts. To improve the energy management, an innovative anomaly detection system should aim at analyzing raw data, detect any kind of anomalies (point, contextual, collective) in an open environment, at large scale. The developed AI system is called SANDMAN (semi-Supervised ANomaly Detection with Multi-AgeNt systems). The system is semi-supervised by an expert of the field who confirms or overturns the feedback of SANDMAN. It processes data in a time constrained manner to detect anomalies as early as possible. SANDMAN is based on the paradigm of self-adaptive multi-agent system. The results show the robustness of the AI regarding the detection of noisy data, of different types of anomalies, and the scaling.  

Scientific goal

Anomalies detection in smart buildings streaming data by a semi-supervised multi-agent system.