The authors take full responsibility for the interpretation of data received from respondents and any errors that may have resulted. v Table of Contents ACKNOWLEDGEMENTS .

The work described in this analysis was funded by the U.S. Department of Energy Office of Energy Efficiency and Renewable Energy (EERE) under Contract No. DE-AC02-05CH11231. We gratefully acknowledge Kurmit Rockwell, Schuyler Schell, and Leslie Nicholls (DOE-FEMP) for providing resources to conduct the analysis. Alice Dasek (DOE-OWIP) suggested the importance of presenting disaggregated market potential estimates. We also thank Dr. Timothy Unruh (DOE Office of Renewable Power) for his longtime support of our research into the U.S. ESCO industry. We thank Donald Gilligan (National Association of Energy Services Companies), Bob Slattery (Oak Ridge National Laboratory), Sharon Conger (General Services Administration), and two anonymous reviewers for providing valuable information and insight into some of the underlying assumptions behind this analysis. Andy Satchwell (LBNL) provided independent peer-review of this manuscript. Finally, we would like to gratefully acknowledge key staff at the ESCOs who donated time responding to requests for information-including providing estimates of market penetration. Any remaining omissions and errors are the responsibility of the authors. LBNL and the U.S. ESCO IndustryFor more than twenty five years, the U.S. Department of Energy has supported Lawrence Berkeley National Laboratory (LBNL) to conduct applied research and provide technical assistance on topics related to the U.S. energy services company (ESCO) industry. LBNL activities include, but are not limited to: the production of triennial reports that estimate the size of the industry; assisting in the design of savings measurement and verification protocols; managing the largest database of ESCO projects in the world; and developing the eProject Builder system (ePB). ePB enables ESCOs and their customers to simulate project cash flow scenarios, securely upload project-level information, and track progress over the life of the energy savings performance contract.

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I hope you will take the time to review this survey of what state utillty commissions are doing to incorporate environmental externalitles Into the regula_rT:: process. The NARUC Energy Conservation Committee asked Lawrence Berkeley Laboratory to undertake this survey and wishes to express its deep appreciation for the work done by Lawrence Berkeley Laboratory. The Energy Conservation Committee with the assistance of the Department of Energy also initiated a conference on environmental externalities in New England in 1989 and wlll sponsor a nationwide conference in Jackson Hole, Wyoming October 1-3, 1990. Efforts by commissions to address environmental externallties are escalating rapidly as national attention has focused on acid rain legislation and possible strategies for dealing wlth the threat of This survey provides a useful snapshot of this i global warming.

The rollout of smart meters in the last several years has opened up new forms of previously unavailable energy data. Many utilities are now able in real-time to capture granular, household level interval usage data at very high-frequency levels for a large proportion of their residential and small commercial customer population. This can be linked to other time and locationspecific information, providing vast, constantly growing streams of rich data (sometimes referred to by the recently popular buzz word, "big data"). Within the energy industry there is increasing interest in tapping into the opportunities that these data can provide.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 interesting insights out of this highfrequency, human-focused data. We at LBNL are calling this "behavior analytics". This kind of analytics has the potential to provide tremendous value to a wide range of energy programs.For example, highly disaggregated and heterogeneous information about actual energy use would allow energy efficiency (EE) and/or demand response (DR) program implementers to target specific programs to specific households; would enable evaluation, measurement and verification (EM&V) of energy efficiency programs to be performed on a much shorter time horizon than was previously possible; and would provide better insights in to the energy and peak hour savings associated with specifics types of EE and DR programs (e.g., behavior-based (BB) programs).In this series, "Insights from Smart Meters", we will present concrete, illustrative examples of the type of value that insights from behavior analytics of these data can provide (as well as pointing out its limitations). We will supply several types of key findings, including:• Novel results, which answer questions the industry previously was unable to answer;• Proof-of-concept analytics tools that can be adapted and used by others; and • Guidelines and protocols that summarize analytical best practices.The goal of this series is to enable evidence-based and data-driven decision making by policy makers and industry stakeholders, including program planners, program designers, program administrators, utilities, commissioners, regulators, and evaluators. This series is one of the products we are employing to achieve this goal. DRAFT -DO NOT CIRCULATE Focus on: The Potential for Peak Hour Savings from Behavior-Based ProgramsThis report focuses on one example of the kind of value that analysis of this data can provide:insights into whether behavior-based (BB) efficiency programs have the potential to provide peak-hour energy savings. This is important because there is increasing interest in using BB programs as a stand-alone peak reduction program, as well as integrating behavior-based strategies into residential incentive-based demand response (DR) programs and time-based retail rates as a way to augment...