Abstract
The world’s fresh water supply is rapidly dwindling. Informing homeowners of their water use patterns can help them reduce consumption. Today’s smart meters only show a whole house’s water consumption over time. People need to be able to see where they are using water most to be able to change their habits. We are the first to present work where appliance water consumption is non-intrusively disaggregated using the results from a non-intrusive load monitoring algorithm. Unlike previous works that require the installation of water sub-meters or water sensors, our method does not. Further, our method uses low-frequency data from standardized meters and does not rely on labelled data. We modify the Viterbi Algorithm to apply a supervised method to an unsupervised disaggregation problem. We are able to achieve very high accuracy results having mean squared errors of under 0.02 L\(^2\)/min\(^2\).
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Notes
- 1.
Classification measures [8] were not used. As NILM has predetermined classification for us, we only need to measure the amount of error in our results.
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Acknowledgments
Research was supported by NSERC, including an Alexander Graham Bell Canada Graduate Scholarship, and a number of awards given by SFU’s Dean of Graduate Studies: a Provost Prize of Distinction, a C.D. Nelson Memorial Graduate Scholarship, and a Graduate Fellowship.
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© 2016 ICST Institute for Computer Sciences, Social Informatics and Telecommunications Engineering
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Ellert, B., Makonin, S., Popowich, F. (2016). Appliance Water Disaggregation via Non-intrusive Load Monitoring (NILM). In: Leon-Garcia, A., et al. Smart City 360°. SmartCity 360 SmartCity 360 2016 2015. Lecture Notes of the Institute for Computer Sciences, Social Informatics and Telecommunications Engineering, vol 166. Springer, Cham. https://doi.org/10.1007/978-3-319-33681-7_38
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DOI: https://doi.org/10.1007/978-3-319-33681-7_38
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