The Functional Latent Block Model for the Co-Clustering of Electricity Consumption Curves

Journal of the Royal Statistical Society. Series C: Applied Statistics - Tập 67 Số 4 - Trang 897-915 - 2018
Charles Bouveyron1,2, Laurent Bozzi3, Julien Jacques4, François-Xavier Jollois5
1Institut National de Recherche en Informatique et en Automatique Sophia Antipolis and , Nice , France
2Université Côte d’Azur, Nice, France
3Électricité de France , Paris-Saclay , France
4Université de Lyon, France
5Université Paris-Descartes, France

Tóm tắt

SummaryAs a consequence of recent policies for smart meter development, electricity operators nowadays can collect data on electricity consumption widely and with a high frequency. This is in particular so in France where the leading electricity company Électricité de France will be able soon to record the consumption of its 27 million clients remotely every 30 min. We propose in this work a new co-clustering methodology, based on the functional latent block model (LBM), which enables us to build ‘summaries’ of these large consumption data through co-clustering. The functional LBM extends the usual LBM to the functional case by assuming that the curves of one block live in a low dimensional functional subspace. Thus, the functional LBM can model and cluster large data sets with high frequency curves. A stochastic expectation–maximization–Gibbs algorithm is proposed for model inference. An integrated information likelihood criterion is also derived to address the problem of choosing the number of row and column groups. Numerical experiments on simulated and original Linky data show the usefulness of the methodology proposed.

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Thông tin xuất bản

Journal of the Royal Statistical Society. Series C: Applied Statistics

Tập 67 Số 4

897-915

Thông tin tác giả