Efficient Incremental Smart Grid Data Analytics
David Xi Cheng, Wojciech Golab, Paul A. S. Ward · EDBT/ICDT Workshops · 2016
Analytical computations over energy data are gaining popularity thanks to the growing adoption of smart electricity meters. Computations in this context range from seemingly straightforward tasks such as calculating monthly bills based on time-of-use pricing, to elaborate building for predictions and recommendations in an eort to reduce peak demand. While research in this promising area is progressing steadily, published algorithms and prototypes have largely avoided the important practical question of how to deal eciently with the incremental nature of energy data, for example per-hour readings produced by smart electricity meters. As a stepping stone towards a comprehensive solution to this problem, we investigate incremental techniques for disaggregating dierent categories of energy consumption, such as base load versus activity load, from hourly smart meter data using the popular \three-line model of Birt et al. Our software prototype, called Insparq, exhibits speedups in excess of 2x for data sets up to tens of GB in size, compared to a naive implementation on top of a conventional scalable batch processing framework.