Time Series Clustering for Demand Response An Online Algorithmic Approach

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. To improve sustainability in Smart Grids, realtime data analytics challenges induced by high volume and high dimensional context-based data need to be addressed. Particularly, with the emerging Smart Grid technologies becoming more ubiquitous, analytics for discovering the underlying structure of high dimensional 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 an online time series clustering approach to systematically and efficiently manage the energy consumption data deluge, and also capture specific behavior i.e., identify households with similar lifestyle patterns. Customers can in this way be segmented into several groups that can be effectively used to enhance DR policies for real time automatic control in the cyberphysical Smart Grid system. Due to the inherent intractability of the ‘optimal clustering’ problem, we propose a novel randomized approximation clustering scheme of electricity consumption data, aiming at addressing three major issues: (i) designing a resource-constrained, online clustering technique for high volume, high dimensional time series data (ii) determining the optimal number of clusters that gives the best approximate clustering configuration, and (iii) providing strong clustering performance guarantees. By the term ‘performance guarantees’, we imply algorithm performance with respect to the best clustering possible for the given data. Our proposed online clustering algorithm is time efficient, achieves a clustering configuration that is optimal within provable worst case approximation factors, scales to large data sets, and is extensible to parallel and distributed architectures. The applicability of our algorithm goes beyond that of the Smart Grid and includes any scenario where clustering needs to be done on high volume and in real-time under space and time constraints.