Reducing the Intrusiveness of Energy and Water End-Use Disaggregation via Social Media and Users Interactions

Abstract

Water end-use disaggregation from smart-metered water consumption data is key to design water demand management strategies. Yet, despite large investments for smart metering networks, few water utilities are actually exploiting the potential of end-use-based consumer profiling. This is mostly due to the intrusiveness of existing disaggregation algorithms, which require intensive and time consuming human interaction for extracting appliance-specific information and estimating the end-use patterns. We propose to explore the potential for social media in facilitating the interactions between water utilities and water users in order to collect consumption diaries. The evaluation of the value of this user-generated information is performed by using the collected diaries as input for a disaggregation algorithm, which is shown to attain acceptable performance in reproducing the end-use patterns.