1 Abstract—This paper presents a thorough analysis of 15- minute residential meter data sets to identify possible value propositions of smart meter measurements. Meter measurements of 50 houses were used to derive a few key data signatures for several target applications such as identifying demand response potentials, detecting abnormal load behaviors, and fault diagnosis. Results showed that for different applications, the communication needs from meters to control centers, data storage capabilities, and the complexity of data processing intelligence varies significantly. Therefore, it is important to build a dynamic data signature database and optimize the distribution of data processing capability between local devices and control centers to avoid communication congestion and to identify problems early. This paper also demonstrates that high- resolution smart meter data can make distribution power grids more economical, reliable, and resilient. Index Terms—smart grid, distribution networks, power grid operation, information management systems, cyber security, integrated distribution energy management, interoperability, data integrity. I. INTRODUCTION HE U.S. government and private industry are investing billions of dollars to build the smart grid infrastructure to save energy, reduce cost, and increase reliability. The wide deployment of modern information technology into power grid control and communication networks makes higher resolution measurements available to more equipment at wider areas compared to the past. As shown in Fig. 1, for example, a smart meter collects data by the minute while the old mechanical meter collects data hourly or monthly; a phasor measurement unit (PMU) collects 30-60 data points per second, much faster than the 1 data point per 1-2 second sampling rate of the traditional supervisory control and data acquisition (SCADA) system. Thus, today’s grid operators have an unprecedented amount of data, which, in theory, should provide the status of the massive number of devices connected to the power grid, This work is supported by the Pacific Northwest National Laboratory, operated for the U.S. Department of Energy by Battelle under Contract DE- AC05-76RL01830. N. Lu, P. Du, X. Guo, and F. Greitzer are with Pacific Northwest National Laboratory, P.O. Box 999, MSIN: K1-85, Richland, WA 99352, USA. Emails: ning.lu@pnnl.gov; Pengwei.du@pnnl.gov; Xinxin.guo@pnnl.gov; frand.greitzer@pnnl.gov. such as generators, breakers, or even individual appliances in commercial buildings or residential houses. However, large volumes of data from multiple sensor networks across transmission and distribution systems may clog the communication network and overwhelm operators if actionable information cannot be generated in a timely manner and at the right priority. In addition, traditionally, data from different sources (such as electricity prices, billing information, weather conditions, PMUs, SCADA, and smart meter measurements) are collected and owned by different departments, even within a single utility. These data are not shared to generate a multi-angle (multi-dimensional) view. Therefore, for the industry to truly benefit from the smart grid investment, it is critical that the massive amount of data made available by smart grid technologies be transformed into useful information in an organized, coordinated, and prioritized manner that helps grid operators make timely decisions to operate the grid safely, economically, and reliably. Fig. 1: Configuration of smart grid communication and control systems In this paper, we focus on processing smart meter data, with the aid of SCADA, billing, and weather data. A 15- minute meter and weather data set collected by researchers at Pacific Northwest National Laboratory (PNNL) [1] was used. A virtual feeder was constructed using the standard IEEE 13- bus model. Billing data were constructed using historical meter data. The goal of the paper is to demonstrate that by analyzing data characteristics, extracting key data signatures, and synthesizing information from all relevant data sources to describe operation status as normal or abnormal, actionable information can be generated to provide grid operators with a Smart Meter Data Analysis Ning Lu, Pengwei Du, Xinxin Guo, and Frank L. Greitzer T 978-1-4673-1935-5/12/$31.00 ©2012 IEEE