Empirical Characterization, Modeling, and Analysis of Smart Meter Data

Smart meter deployments are spurring renewed interest in analysis techniques for electricity usage data. However, an important prerequisite for data analysis is characterizing and modeling how electrical loads use power. While prior work has made significant progress in deriving insights from electricity data, one issue that limits accuracy is the use of general and often simplistic load models. Prior models often associate a fixed power level with an "on" state and either no power, or some minimal amount, with an "off" state. This paper's goal is to develop a new methodology for modeling electric loads that is both simple and accurate. Our approach is empirical in nature: we monitor a wide variety of common loads to distill a small number of common usage characteristics, which we then leverage to construct accurate load-specific models. We show that our models are significantly more accurate than binary on-off models, decreasing the root mean square error by as much as 8x for representative loads. Finally, we demonstrate three novel applications that use our empirical load models to analyze and derive insights from smart meter data, including i) generating device-accurate synthetic traces of building electricity usage; ii) filtering out loads that generate rapid and random power variations in smart meter data; and iii) detecting the presence of specific load models in time-series power data.