Towards Dynamic Demand Response on Efficient Consumer Grouping Algorithmics

The widespread monitoring of electricity consumption due to increasingly pervasive deployment of networked sensors in urban environments has resulted in an unprecedentedly large volume of data being collected. Particularly, with the emerging Smart Grid technologies becoming more ubiquitous, real-time and online analytics for discovering the underlying structure of increasing-dimensional (w.r.t. time) consumer time series data are crucial to convert the massive amount of fine-grained energy information gathered from residential smart meters into appropriate demand response (DR) insights. In this paper we propose READER and OPTIC, that are real-time and online algorithmic pre-processing frameworks respectively, for effective DR in the Smart Grid. READER (OPTIC) helps discover underlying structure from increasing-dimensional consumer consumption time series data in a provably optimal real-time (online) fashion. READER (OPTIC) catalyzes the efficacy of DR programs by systematically and efficiently managing the energy consumption data deluge, at the same time capturing in real-time (online), specific behavior, i.e., households or time instants with similar consumption patterns. The primary feature of READER (OPTIC) is a real-time (online) randomized approximation algorithm for grouping consumers based on their electricity consumption time series data, and provides two crucial benefits: (i) time efficiently tackles high volume, increasing-dimensional time series data and (ii) provides provable worst case grouping performance guarantees. We validate the grouping and DR efficacy of READER and OPTIC via extensive experiments conducted on both, a USC microgrid dataset as well as a synthetically generated dataset.