Picture of smart phone in human hand

World leading smartphone and mobile technology research at Strathclyde...

The Strathprints institutional repository is a digital archive of University of Strathclyde's Open Access research outputs. Strathprints provides access to thousands of Open Access research papers by University of Strathclyde researchers, including by Strathclyde researchers from the Department of Computer & Information Sciences involved in researching exciting new applications for mobile and smartphone technology. But the transformative application of mobile technologies is also the focus of research within disciplines as diverse as Electronic & Electrical Engineering, Marketing, Human Resource Management and Biomedical Enginering, among others.

Explore Strathclyde's Open Access research on smartphone technology now...

A graph-based signal processing approach for low-rate energy disaggregation

Stankovic, Vladimir and Liao, Jing and Stankovic, Lina (2014) A graph-based signal processing approach for low-rate energy disaggregation. In: 2014 IEEE Symposium on Computational Intelligence for Engineering Solutions (CIES) Proceedings. UNSPECIFIED, Piscataway, NJ, pp. 81-87. ISBN 9781479945108

PDF (Stankovic-etal-CIES2014-graph-based-signal-processing-approach-for-low-rate-energy-disaggregation)
Stankovic_etal_CIES2014_graph_based_signal_processing_approach_for_low_rate_energy_disaggregation.pdf - Accepted Author Manuscript

Download (221kB) | Preview


Graph-based signal processing (GSP) is an emerging field that is based on representing a dataset using a discrete signal indexed by a graph. Inspired by the recent success of GSP in image processing and signal filtering, in this paper, we demonstrate how GSP can be applied to non-intrusive appliance load monitoring (NALM) due to smoothness of appliance load signatures. NALM refers to disaggregating total energy consumption in the house down to individual appliances used. At low sampling rates, in the order of minutes, NALM is a difficult problem, due to significant random noise, unknown base load, many household appliances that have similar power signatures, and the fact that most domestic appliances (for example, microwave, toaster), have usual operation of just over a minute. In this paper, we proposed a different NALM approach to more traditional approaches, by representing the dataset of active power signatures using a graph signal. We develop a regularization on graph approach where by maximizing smoothness of the underlying graph signal, we are able to perform disaggregation. Simulation results using publicly available REDD dataset demonstrate potential of the GSP for energy disaggregation and competitive performance with respect to more complex Hidden Markov Model-based approaches.