Water Fixture Identification in Smart Housing: A Domain Knowledge Based Case Study
Yan Gao, Daqing Hou, Natasha Kholgade Banerjee, Sean K. Banerjee · 2016
In current practice, smart housing environments often over-install smart sensors on every fixture and log data from them at high sampling rates, resulting in more data being collected than is necessary. Fixture identification offers a possible alternative to reduce the number of sensors installed and the amount of data collected in smart housing. Fixture identification applies classifiers to label utility consumption data aggregated at the apartment level by the specific fixture that actually contributes the data, such as the shower or the kitchen sink. Successful fixture identification can be used to educate tenants, optimize the resource supply strategy, and offer a smart solution for detecting abnormal usage activities. In this paper, we report a case study of water fixture identification by using support vector machines (SVMs) to perform fixture classification. We use the Smart Housing Dataset from Clarkson University, which comprises of one academic year of tenant activities from 12 student apartments. Our results show that the proposed approach achieves an average accuracy between 78% to 87.8% for identifying hot water fixtures including kitchen sink, bathroom sink and shower. As a result, the number of smart meters per apartment is reduced from 7 to 3, one for hot water, one for cold water, and the third for toilet. The novelty of our study lies in the feature selection process, which is guided by our domain knowledge of water fixture characteristics and the correlation between water fixture usage and other user behavior in the apartments. We describe our proposed features, their rationale, and their effect on classification performance.