Open AccessElectrical Consumption Patterns through Machine Learning
Author(s) -
Amelec Viloria,
Alexa Naveda,
Hugo Hernández Palma,
William Niebles Núñez,
Leonardo Niebles Núñez
Publication year - 2020
Publication title -
journal of physics. conference series
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.21
H-Index - 85
eISSN - 1742-6596
pISSN - 1742-6588
DOI - 10.1088/1742-6596/1432/1/012093
Subject(s) - metering mode , consumption (sociology) , computer science , electricity , process (computing) , data science , engineering , electrical engineering , mechanical engineering , social science , sociology , operating system
Electricity distribution companies have been incorporating new technologies that allow them to obtain complete information in real time about their customers´ consumption. Thus, a new concept called “Smart Metering” has been adopted, giving way to new types of meters that interact in an interconnected system. This will allow to make data analysis, accurate forecasts and detecting consumption patterns that will be relevant for the decision-making process. This research focuses on discovering common patterns among customers from data collected by smart meters.