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Clustering electricity consumers using high‐dimensional regression mixture models
Author(s) -
Devijver Emilie,
Goude Yannig,
Poggi JeanMichel
Publication year - 2019
Publication title -
applied stochastic models in business and industry
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.413
H-Index - 40
eISSN - 1526-4025
pISSN - 1524-1904
DOI - 10.1002/asmb.2453
Subject(s) - cluster analysis , metering mode , computer science , smart meter , regression , profiling (computer programming) , data mining , smart grid , regression analysis , electricity , slicing , artificial intelligence , machine learning , statistics , engineering , mathematics , mechanical engineering , electrical engineering , world wide web , operating system
A massive amount of data about individual electrical consumptions are now provided with new metering technologies and smart grids. These new data are especially useful for load profiling and load modeling at different scales of the electrical network. A new methodology based on mixture of high‐dimensional regression models is used to perform clustering of individual customers. It leads to uncovering clusters corresponding to different regression models. Temporal information is incorporated in order to prepare the next step, the fit of a forecasting model in each cluster. Only the electrical signal is involved, slicing the electrical signal into consecutive curves to consider it as a discrete time series of curves. Interpretation of the models is given on a real smart meter dataset of Irish customers.