Abstract
This paper presents the application of clustering algorithms to 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 obtained with the two algorithms are analyzed and compared. This study represents the first step towards the development of a prediction model for energy consumption.
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© 2016 Springer International Publishing Switzerland
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Melzi, F.N., Zayani, M.H., Benhamida, A., Stephan, F., Same, A., Oukhellou, L. (2016). Towards Smart City Energy Analytics: Identification of Consumption Patterns Based on the Clustering of Daily Electric Consumption Curves. In: Auvray, G., Bocquet, JC., Bonjour, E., Krob, D. (eds) Complex Systems Design & Management. Springer, Cham. https://doi.org/10.1007/978-3-319-26109-6_37
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DOI: https://doi.org/10.1007/978-3-319-26109-6_37
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Publisher Name: Springer, Cham
Print ISBN: 978-3-319-26107-2
Online ISBN: 978-3-319-26109-6
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