Energy efficiency and sustainability are important factors to address in the context of smart cities. In this sense, a necessary functionality is to reveal various preferences, behaviors, and characteristics of individual consumers, considering the energy consumption information from smart meters. In this paper, we introduce a general methodology and a specific two-level clustering approach that can be used to group, considering global and local features, energy consumptions and productions of households. Thus, characteristic load and production profiles can be determined for each consumer and prosumer, respectively. The obtained results will be generally applicable and will be useful in a general business analytics context.
Arco, L, Casas Cardoso, GM & Nowe, A 2020, 'Two-level clustering methodology for smart metering data', Cuadernos de Administración, vol. 33. https://doi.org/10.11144/Javeriana.cao33.tlcms, https://doi.org/10.11144/Javeriana.cao33.tlcms
Arco, L., Casas Cardoso, G. M., & Nowe, A. (2020). Two-level clustering methodology for smart metering data. Cuadernos de Administración, 33. https://doi.org/10.11144/Javeriana.cao33.tlcms, https://doi.org/10.11144/Javeriana.cao33.tlcms
@article{a0c902790afe4acf86968e0d2f7ab9a5,
title = "Two-level clustering methodology for smart metering data",
abstract = "Energy efficiency and sustainability are important factors to address in the context of smart cities. In this sense, a necessary functionality is to reveal various preferences, behaviors, and characteristics of individual consumers, considering the energy consumption information from smart meters. In this paper, we introduce a general methodology and a specific two-level clustering approach that can be used to group, considering global and local features, energy consumptions and productions of households. Thus, characteristic load and production profiles can be determined for each consumer and prosumer, respectively. The obtained results will be generally applicable and will be useful in a general business analytics context.",
keywords = "clustering, time series, smart metering",
author = "Leticia Arco and {Casas Cardoso}, {Gladys Mar{\'i}a} and Ann Nowe",
year = "2020",
month = apr,
day = "28",
doi = "10.11144/Javeriana.cao33.tlcms",
language = "English",
volume = "33",
journal = "Cuadernos de Administraci{\'o}n",
issn = "0120-3592",
publisher = "Pontificia Universidad Javeriana, Bogota",
}