Full Title: Insights from Smart Meters: Ramp-up, Dependability, and Short-Term Persistence of Savings from Home Energy Reports
Author(s): Todd, Annika, Michael Perry, Brian Smith, Michael J. Sullivan, Peter Cappers, and Charles A. Goldman
Publisher(s): Lawrence Berkeley National Laboratory
Publication Date: 04/2015
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Smart meters, smart thermostats, and other new technologies provide previously unavailable high-frequency and location-specific energy usage data. Many utilities are now able to capture real-time, customer specific hourly interval usage data for a large proportion of their residential and small commercial customers. These vast, constantly growing streams of rich data (or, “big data”) have the potential to provide novel insights into key policy questions about how people make energy decisions.
What can we do with all of these data? The richness and granularity of these data enable many types of creative and cutting-edge analytics. Technically sophisticated and rigorous statistical techniques can be used to pull useful insights out of this high-frequency, human-focused data. In this series, we call this “behavior analytics.” This kind of analytics has the potential to provide tremendous value to a wide range of energy programs. For example, disaggregated and heterogeneous information about actual energy use allows energy efficiency (EE) and/or demand response (DR) program implementers to target specific programs to specific households; enables evaluation, measurement and verification (EM&V) of energy efficiency programs to be performed on a much shorter time horizon than was previously possible; and may provide better insights into the energy and peak hour savings associated with EE and DR programs (e.g., behavior-based (BB) programs).