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 functional Kmeans 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.
Type de document :
Communication dans un congrès
CSD&M 2015 - 6th International Conference on Complex Systems Design and Management, Nov 2015, PARIS, France. 12p, 2015
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https://hal.archives-ouvertes.fr/hal-01212957
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Soumis le : mercredi 7 octobre 2015 - 15:30:51
Dernière modification le : mercredi 11 avril 2018 - 12:10:02

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  • HAL Id : hal-01212957, version 1

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Fateh Nassim Melzi, Mohamed Haykel Zayani, Amira Benhamida, François Stephan, Allou Same, et al.. Towards Smart City Energy Analytics: Identification of Electric Consumption Patterns Based on Clustering Approaches. CSD&M 2015 - 6th International Conference on Complex Systems Design and Management, Nov 2015, PARIS, France. 12p, 2015. 〈hal-01212957〉

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