Towards Smart City Energy Analytics: Identification of Electric Consumption Patterns Based on Clustering Approaches

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 K-means algorithm 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 a prediction models for energy consumption.
Type de document :
Communication dans un congrès
CSD&M 2014 - 5th International conference on Complex Systems Design and Management, Nov 2014, PARIS, France. 12p, 2014
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https://hal.archives-ouvertes.fr/hal-01217400
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Soumis le : lundi 19 octobre 2015 - 15:21:23
Dernière modification le : mercredi 11 avril 2018 - 12:10:02

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  • HAL Id : hal-01217400, version 1

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Fateh Nassim Melzi, Mohamed Haykel Zayani, Amira Ben Hamida, François Stephan, Allou Same, et al.. Towards Smart City Energy Analytics: Identification of Electric Consumption Patterns Based on Clustering Approaches. CSD&M 2014 - 5th International conference on Complex Systems Design and Management, Nov 2014, PARIS, France. 12p, 2014. 〈hal-01217400〉

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