28 PERVASIVE computing Published by the IEEE CS n 1536-1268/11/$26.00 © 2011 IEEE SMART ENERGY SYSTEMS I magine an energy feedback system that displays not only total power consump- tion and cost, but also suggests spe- cifc cost-effective measures to improve energy ef fciency. Such a system could report, for example, “Based on your energy con- sumption patterns, you could save US$360 per year by upgrading to a more effcient refrigera- tor, which would pay for itself after 21 months.” The challenge in this scenario is how to sense end uses of energy to provide feedback at the indi- vidual device or appliance level. Emerging smart meters promise a tighter tem- poral coupling between energy usage and feedback (down to 15-minute sampling intervals). However, the focus still is on aggregate consumption, mak- ing it dif fcult for consumers to ascertain which devices or appliances are responsible for their energy usage. Disag- gregated end-use energy data promises to transform the way residents, utilities, and policy makers think about and understand how energy is consumed in the home. Our research team and many others world- wide are working toward a new generation of electricity, water, and natural gas measurement systems that are low cost, easy to install, and most important, capable of providing disaggre- gated data about consumption at the individual appliance or device level. Our team’s contribu- tions are focused on approaches for obtaining this disaggregated data from a single sensing point. Our vision is to provide high granular- ity resource-sensing systems for homes and businesses that will fundamentally transform how electricity, water, and natural gas are un- derstood, studied, and ultimately consumed. This article focuses on electrical energy, but we’ve also developed systems for disaggregat- ing water and gas usage (see the “Water and Gas” sidebar). All three of our systems share a common approach: they monitor side effects of resource usage that are manifest throughout a home’s internal electricity, plumbing, or gas infrastructure. Although our techniques should function in commercial and industrial sectors, we’ve concentrated so far on validating our meth- ods in the residential sector, which presents many challenges. In addition to the signifcant amount of energy use and CO 2 emissions in the residential sector, 1,2 there’s a higher degree of decentralized ownership. Also, levels of self- interest and expertise in reducing energy con- sumption vary, compared with the industrial and commercial sectors. Perhaps more compel- ling, however, is that energy consumption can vary widely from home to home based simply This article surveys existing and emerging disaggregation techniques for energy-consumption data and highlights signal features that might be used to sense disaggregated data in an easily installed and cost-effective manner. Jon Froehlich, Eric Larson, Sidhant Gupta, and Gabe Cohn University of Washington Matthew S. Reynolds Duke University Shwetak N. Patel University of Washington Disaggregated End-Use Energy Sensing for the Smart Grid