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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.
Keywords :
CLUSTERING
K-MEAN
https://hal.archives-ouvertes.fr/hal-01215017
Contributor : Ifsttar Cadic Connect in order to contact the contributor
Submitted on : Tuesday, October 13, 2015 - 2:56:14 PM
Last modification on : Saturday, January 15, 2022 - 3:55:10 AM
Contributor : Ifsttar Cadic Connect in order to contact the contributor
Submitted on : Tuesday, October 13, 2015 - 2:56:14 PM
Last modification on : Saturday, January 15, 2022 - 3:55:10 AM