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

As 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 Electricite 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.