Clustering electricity consumers using high-dimensional regression mixture models

DOI10.1002/ASMB.2453MaRDI QIDQ6576828

Emilie Devijver, Yannig Goude, Jean-Michel Poggi

Publication date: 23 July 2024

Published in: Applied Stochastic Models in Business and Industry (Search for Journal in Brave)

zbMATH Keywords

clusteringmixture modelsindividual electricity consumers

Mathematics Subject Classification ID

Statistics (62-XX)

Cites Work

• Title not available (Why is that?)
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• \(\ell_{1}\)-penalization for mixture regression models
• Slope heuristics: overview and implementation
• Finite mixture regression: a sparse variable selection by model selection for clustering
• Estimating the dimension of a model
• Minimal penalties for Gaussian model selection
• Model-based regression clustering for high-dimensional data: application to functional data
• Optimized clusters for disaggregated electricity load forecasting
• Unsupervised learning of regression mixture models with unknown number of components
• A new look at the statistical model identification

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