A System Design for Automated Tailoring of Behavior Change Recommendations Using Time-Series Clustering of Energy Consumption Data

In this paper we describe our approach to address the challenges of tailoring and personalizing behavior change recommendations based on energy consumption data collected through smart meters and energy monitoring tech-nologies. The approach uses time-series clustering techniques with dynamic time warping to group daily energy consumption curves into similar clusters, and then provides personalized recommendations for shifting energy behavior to each in-dividual based on their predicted consumption pattern, the day-ahead energy prices and the resulting savings opportunities. The paper presents the methodol-ogy and discusses the suitability of this approach for improving traditional energy feedback and demand response interventions, and provides an outlook on the possibilities of artificial intelligence methods to further improve the concept.