Title :
SMAS: A smart meter data analytics system
Author :
Xiufeng Liu ; Golab, Lukasz ; Ilyas, Ihab F.
Author_Institution :
Univ. of Waterloo, Waterloo, ON, Canada
Abstract :
Smart electricity meters are replacing conventional meters worldwide and have enabled a new application domain: smart meter data analytics. In this paper, we introduce SMAS, our smart meter analytics system, which demonstrates the actionable insight that consumers and utilities can obtain from smart meter data. Notably, we implemented SMAS inside a relational database management system using open source tools: PostgreSQL and the MADLib machine learning toolkit. In the proposed demonstration, conference attendees will interact with SMAS as electricity providers, consultants and consumers, and will perform various analyses on real data sets.
Keywords :
SQL; data analysis; learning (artificial intelligence); power engineering computing; relational databases; smart meters; MADLib machine learning toolkit; PostgreSQL; SMAS; open source tools; relational database management system; smart electricity meters; smart meter data analytics system; Cooling; Data analysis; Databases; Feature extraction; Forecasting; Smart meters; Temperature sensors;
Conference_Titel :
Data Engineering (ICDE), 2015 IEEE 31st International Conference on
Conference_Location :
Seoul
DOI :
10.1109/ICDE.2015.7113405
