Open AccessTwo-level clustering methodology for smart metering data
Author(s) -
Leticia Arco García,
Gladys M. Casas Cardoso,
Ann Nowé
Publication year - 2020
Publication title -
cuadernos de administración
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.109
H-Index - 9
ISSN - 1900-7205
DOI - 10.11144/javeriana.cao33.tlcms
Subject(s) - prosumer , cluster analysis , context (archaeology) , computer science , sustainability , metering mode , energy consumption , analytics , production (economics) , consumption (sociology) , big data , energy (signal processing) , environmental economics , data mining , engineering , mathematics , artificial intelligence , geography , renewable energy , statistics , economics , mechanical engineering , ecology , social science , macroeconomics , archaeology , sociology , electrical engineering , biology
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.