Picture of athlete cycling

Open Access research with a real impact on health...

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 Strathclyde researchers, including by researchers from the Physical Activity for Health Group based within the School of Psychological Sciences & Health. Research here seeks to better understand how and why physical activity improves health, gain a better understanding of the amount, intensity, and type of physical activity needed for health benefits, and evaluate the effect of interventions to promote physical activity.

Explore open research content by Physical Activity for Health...

Blind non-intrusive appliance load monitoring using graph-based signal processing

Zhao, Bochao and Stankovic, Lina and Stankovic, Vladimir (2015) Blind non-intrusive appliance load monitoring using graph-based signal processing. In: GLOBALSIP-2015, 2015-12-14 - 2015-12-16, FL.

[img]
Preview
Text (Zhao-etal-GLOBALSIP2015-Blind-non-intrusive-appliance-load-monitoring-using-graph-based-signal-processing)
Zhao_etal_GLOBALSIP2015_Blind_non_intrusive_appliance_load_monitoring_using_graph_based_signal_processing.pdf - Accepted Author Manuscript

Download (143kB) | Preview

Abstract

With ongoing massive smart energy metering deployments, disaggregation of household's total energy consumption down to individual appliances using purely software tools, aka. non-intrusive appliance load monitoring (NALM), has generated increased interest. However, despite the fact that NALM was proposed over 30 years ago, there are still many open challenges. Indeed, the majority of approaches require training and are sensitive to appliance changes requiring regular re-training. In this paper, we tackle this challenge by proposing a 'blind' NALM approach that does not require any training. The main idea is to build upon an emerging field of graph-based signal processing to perform adaptive threshold-ing, signal clustering and feature matching. Using two datasets of active power measurements with 1min and 8sec resolution, we demonstrate the effectiveness of the proposed method using a state-of-the-art NALM approaches as benchmarks.