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. The task of inferring the breakdown of water-use from smart meter data is called water disaggregation. Water disaggregation has been dominated by studies that rely on high-frequency data, proprietary meters, and/or labelled datasets. In contrast, this thesis uses low-frequency data from standardized meters and does not rely on labelled data. To accomplish this, we leverage information from non-intrusive load monitoring, the electricity counterpart of this task. We propose a modification of the Viterbi Algorithm that applies a supervised method to an unsupervised disaggregation problem. Using this, we are able to achieve mean squared errors of under 0.02 L2/min2.
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Thesis advisor: Popowich, Fred
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