End-Use Load Profiles for the U.S. Building Stock: Market Needs, Use Cases, and Data Gaps

Abstract

States and utilities are developing increasingly ambitious energy goals. Part of the solution to meeting these goals is improving electric grid flexibility. This includes shifting electric demand to align with grid needs. Thus, identifying and using building energy efficiency and other distributed energy resources to produce the highest grid value requires highly resolved, accurate and accessible electricity end-use load profiles (EULPs). EULPs quantify how and when energy is used. Currently, few accurate and accessible end-use load profiles are available for utilities, public utility commissions, state energy offices and other stakeholders to use to prioritize investment and value energy efficiency, demand response, distributed generation and energy storage. Highquality EULPs are also critical for determining the time-sensitive value of efficiency 1 and other distributed energy resources, and the widespread adoption of grid-interactive efficient buildings (GEBs). 2 For example, EULPs can be used to accurately forecast energy savings in buildings or identify energy activities that can be shifted to different times of the day. This report serves as the first-year deliverable for a multiyear U.S. Department of Energy-funded project, End-Use Load Profiles for the U.S. Building Stock, that intends to produce a set of highly resolved EULPs of the U.S. residential and commercial building stock. The project team, made up of researchers from the National Renewable Energy Laboratory (NREL), Lawrence Berkeley National Laboratory (LBNL), and Argonne National Laboratory, 3 ultimately will use calibrated physics-based building energy models to create these EULPs.

Timed to save: the added value of accounting for hourly incidence of electricity savings from residential space-conditioning measures

“…Empirical hourly savings shapes such as those we derive here can provide accurate estimates of value (Mims Frick et al, 2019b). Where these shapes are not available, coincidence factors and peak period The hourly savings shapes presented here demonstrate that residential energy efficiency spaceconditioning measures can help support an evolving electricity grid.…”

“…A lack of utility and efficiency measure performance data currently limits the implementation of these methods. Hourly load and savings shapes generated by building energy simulation models married with detailed building stock data could provide accurate estimates of the non-peak electric efficiency savings (Mims Frick et al, 2019b). 1 However, calibration of these simulations to empirical savings shapes -such as those we estimate here -will be key to their precision, usability, and reliability in utility resource planning and valuation studies.…”

“…This paper builds on existing research on the timing of electric efficiency savings. Mims Frick et al (2019b) identified the importance of end-use load profiles for understanding the time-sensitive value of efficiency and the dependence of that value on location and utility operations. It also documented how the limited availability of these shapes has hindered analysis of the time-sensitive value of efficiency.…”

U.S. Building Stock Characterization Study: A National Typology for Decarbonizing U.S. Buildings

“…A complete description of the ResStock and ComStock methodologies is beyond the scope of this document, but they will be summarized here. For further details on ResStock seeWilson et al (2017), which reflects an older set of data sources, but the methodology is largely the same, and see MimsFrick et al (2019) for ComStock and some updates on ResStock. Results presented here are consistent with the state of ResStock used to produce the End-Use Load Profiles (EULP) dataset v1.0; the output correction model discussed in the EULP report has not been applied to these results(Wilson et al 2021).…”

U.S. Building Stock Characterization Study: A National Typology for Decarbonizing U.S. Buildings

“…A complete description of the ResStock and ComStock methodologies is beyond the scope of this document, but they will be summarized here. For further details on ResStock seeWilson et al (2017), which reflects an older set of data sources, but the methodology is largely the same, and see MimsFrick et al (2019) for ComStock and some updates on ResStock. Results presented here are consistent with the state of ResStock used to produce the End-Use Load Profiles (EULP) dataset v1.0; the output correction model discussed in the EULP report has not been applied to these results(Wilson et al 2021) …”