Identifying Daily Electric Consumption Patterns from Smart Meter Data by Means of Clustering Algorithms

Fateh Nassim Melzi (1) , Mohamed Haykel Zayani (1) , Amira Ben Hamida (1) , Allou Same (2) , Latifa Oukhellou (2)

1 IRT SystemX
2 IFSTTAR/COSYS/GRETTIA - Génie des Réseaux de Transport Terrestres et Informatique Avancée

This paper presents clustering approaches applied on daily energy consumption curves of buildings. Our aim is to identify a reduced set of consumption patterns for a tertiary building during one year. These patterns depend on the temperature throughout the year as well as the type of the day (working day, work-free day and school holidays). Two clustering approaches are used independently, namely the functional K-means algorithm, that takes into account the functional aspect of data and the Expectation-Maximization algorithm based on Gaussian Mixture Model (EM-GMM). The clustering results of the two algorithms are analyzed and compared. This study represents the first step towards the development of prediction models for energy consumption.

Mots clés: CLUSTERING; K-MEAN

Mots clés: CONSOMMATION ENERGETIQUE; VILLE INTELLIGENTE; MODELE DE MELANGE; ALGORITHME ESPERANCE-MAXIMISATION; EXPLORATION DE DONNEES

Domaines: Autre

Fateh Nassim Melzi, Mohamed Haykel Zayani, Amira Ben Hamida, Allou Same, Latifa Oukhellou. Identifying Daily Electric Consumption Patterns from Smart Meter Data by Means of Clustering Algorithms. ICMLA 2015 - IEEE International Conference on Machine Learning and Applications, Dec 2015, MIAMI, United States. 6p. ⟨hal-01215017⟩