Fixture identification from aggregated hot water consumption data

Activity identification in smart housing utilizes smart meters to label consumption of utilities, such as cold and hot water, into human activities, such as cooking and cleaning. Typical approaches utilize a large array of high sampling rate sensors installed at each fixture location. This high density-high sampling rate approach raises computational challenges due to the volume of data generated over time. In this paper, we present a novel approach for identifying water usage patterns using a sparse array of sensors. Unlike traditional approaches which utilize data from individual fixtures, our approach identify fixtures by classifying the aggregated water usage from the kitchen sink, bathroom sink and shower. Furthermore, we model fixture and user characteristics to generate a set of higher level features that are used to identify individual fixtures. We evaluate our approach using a novel dataset of 12 apartments from the Clarkson University Smart Housing Project. Our results show that our approach reduces the number of fixture level smart meters from 7 to 3, while achieving an average accuracy between 70% to 80% for identifying hot water fixtures used in the kitchen sink, bathroom sink and shower.