A comparative study of low sampling non intrusive load dis-aggregation

Kaustav Basu (1) , Ahmad Hably (1) , Vincent Debusschere (2) , Seddik Bacha (2, 3) , Geert Jan Dirven (2) , Andres Ovalle (2)

1 GIPSA-SYSCO - GIPSA - Systèmes non linéaires et complexité
2 G2ELab - Laboratoire de Génie Electrique de Grenoble
3 UGA [2016-2019] - Université Grenoble Alpes [2016-2019]

Non-intrusive load monitoring (NILM) deals with the identification and subsequent energy estimation of the individual appliances from the smart meter data. The state of the art applications typically runs once per day and reports the detected appliances. In this work, data driven models are implemented for two different sampling rates (10 seconds and 15 minutes). The models are trained for 20 houses in the Netherlands and tested for a period of 4-weeks. The results indicate that the disaggregation methods is applicable for both sampling cases but with different use-case.

Mots clés: Energy Management; Smart Grids; Non-intrusive load monitoring; load disaggregation; Signal Processing; Smart Meters

Domaines: Automatique / Robotique; Traitement du signal et de l'image [eess.SP]; Energie électrique

Kaustav Basu, Ahmad Hably, Vincent Debusschere, Seddik Bacha, Geert Jan Dirven, et al.. A comparative study of low sampling non intrusive load dis-aggregation. IECON 2016 - 42nd Annual Conference of the IEEE Industrial Electronics Society, Oct 2016, Florence, Italy. ⟨10.1109/IECON.2016.7793294⟩. ⟨hal-01385572⟩