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...

Power disaggregation of domestic smart meter readings using dynamic time warping

Elafoudi, Georgia and Stankovic, Lina and Stankovic, Vladimir (2014) Power disaggregation of domestic smart meter readings using dynamic time warping. In: 6th International Symposium on Communications, Control and Signal Processing (ISCCSP), 2014. IEEE.

[img] PDF (Elafoudi-etal-2014-Power-disaggregation-of-domestic-smart-meter-readings)
Elafoudi_etal_2014_Power_disaggregation_of_domestic_smart_meter_readings.pdf - Accepted Author Manuscript

Download (170kB)

Abstract

Non-intrusive appliance load monitoring (NALM), also called load disaggregation, is a method for isolating the energy consumption of individual appliances from an overall household energy consumption. Traditionally, NALM techniques are based on measuring several electrical parameters at high sampling rates, which increases the meter cost and communications and storage overhead. In this paper, we propose a low-complexity disaggregation method based on Dynamic Time Warping algorithm that uses only active power aggregate smart meter data, captured at a low frequency, for training and disaggregation . Experimental results are provided for three households using data collected during two months, showing which individual appliances were used and when. Average recognition accuracy of 85% was obtained.