Institute of Information Science, Academia Sinica

Activity analysis disaggregates utility consumption from smart meters into specific usage that associates with human activities. It can not only help residents better manage their consumption for sustainable lifestyle, but also allow utility managers to devise conservation programs. Existing research efforts on disaggregating consumption focus on analyzing consumption features with high sample rates between 1Hz--1MHz. However, many smart meter deployments support sample rates at most 1/900Hz, which challenges activity analysis with occurrences of parallel activities and the lack of detailed consumption features.

In this talk, I will present an unsupervised statistical framework for disaggregation on coarse granular water meter readings based on hidden Markov models. This framework explicitly formulates appliance characteristics and activity correlations, and has been deployed to serve over 300 pilot households in Dubuque, Iowa. The effectiveness of our approach will be demonstrated using both simulation and real world data. I will also discuss some interesting consumption patterns identified, positive behavior changes observed, and overall outcome studies.

Dr. Chang-Tien Lu is an Associate Professor in the Department of Computer Science at Virginia Polytechnic Institute and State University. He served as Program Co-Chair of the 18th IEEE International Conference on Tools with Artificial Intelligence in 2006 and as General Co-Chair of the 20th IEEE International Conference on Tools with Artificial Intelligence in 2008 and the 17th ACM SIGSPATIAL International Conference on Advances in Geographic Information Systems in 2009. He is also serving as Vice Chair of the ACM Special Interest Group on Spatial Information (ACM SIGSPATIAL). Dr. Lu's research work focuses on emerging requirements for analyzing, retrieving, and visualizing massive scientific data. His ongoing projects range from explorations of fundamental access issues to practical applications that deal with data analysis and knowledge discovery tasks. His research has been sponsored by the Department of Defense, Virginia Transportation Research Council, and District Department of Transportation. He received a PhD in Computer Science from the University of Minnesota at Twin Cities. More details are available at http://www.cs.vt.edu/~ctlu/.