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

Résumé : 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.
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Conference papers
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Submitted on : Tuesday, October 13, 2015 - 2:56:14 PM
Last modification on : Wednesday, April 11, 2018 - 12:10:02 PM

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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⟩

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