Clustering electricity consumers using high‐dimensional regression mixture models. (3rd May 2019)

• Record Type: Journal Article
• Authors: Devijver, Emilie; Goude, Yannig; Poggi, Jean‐Michel
• Is Part Of: Applied stochastic models in business and industry. Volume 36:Number 1(2020)
• Page Start: 159
• Page End: 177
• Publication Date: 2019-05-03
• Subjects: clustering -- individual electricity consumers -- mixture models; Stochastic analysis -- Periodicals; Stochastic processes -- Periodicals; Business mathematics -- Periodicals; Finance -- Mathematical models -- Periodicals; Industrial management -- Mathematical models -- Periodicals; 338.00151923
• DOI: 10.1002/asmb.2453
• Languages: English
• ISSNs: 1524-1904

Abstract

Abstract: 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.