  Smart meters are increasingly used worldwide. Smart meters are the advanced
meters capable of measuring energy consumption at a fine-grained time interval,
e.g., every 15 minutes. Smart meter data are typically bundled with social
economic data in analytics, such as meter geographic locations, weather
conditions and user information, which makes the data sets very sizable and the
analytics complex. Data mining and emerging cloud computing technologies make
collecting, processing, and analyzing the so-called big data possible. This
paper proposes an innovative ICT-solution to streamline smart meter data
analytics. The proposed solution offers an information integration pipeline for
ingesting data from smart meters, a scalable platform for processing and mining
big data sets, and a web portal for visualizing analytics results. The
implemented system has a hybrid architecture of using Spark or Hive for big
data processing, and using the machine learning toolkit, MADlib, for doing
in-database data analytics in PostgreSQL database. This paper evaluates the key
technologies of the proposed ICT-solution, and the results show the
effectiveness and efficiency of using the system for both batch and online
analytics.
