Neural Network-Based Residential Water End-Use Disaggregation

Sammanfattning: Sustainable management of finite resources is vital for ensuring livable conditions for both current and future generations. Measuring the total water consumption of residential households at high temporal resolutions and automatically disaggregating the sole signal into classified end usages (e.g. shower, sink) allows for identification of behavioural patterns that could be improved to minimise wasteful water consumption. Such disaggregation is not trivial, as water consuming patterns vary greatly depending on consumer behaviour, and further since at any given time, an unknown amount of fixtures may be used simultaneously. In this work, we approach the disaggregation problem by evaluating the performance of a set of recurrent and convolutional neural network structures provided approximately one year of high resolution water consumption data from a single apartment in Sweden. Unlike previous approaches to the problem, we let the models process the full, uninterrupted flow traces (as opposed to extracted segments of water consuming activity) in order to allow for temporal dependencies within and between water consuming activities to be learned. Out of four networks applied to the task, we find that a deeper temporal convolutional network structure yields the best overall results on the test data, with prediction accuracy of 85% and F1-score above 0.8 averaged over all end-use categories - a performance exceeding that of commercial analysis tools, and comparable to components of current state-of-the-art approaches. However, significant decreases in performance are observed for all of the networks, particularly for toilet and washing machine activity, when evaluating the models on unseen and augmented data from the apartment, indicating the results can not be fully generalised for usage in other households.