End-Use Load Profiles for the U.S. Building Stock: Methodology and Results of Model Calibration, Validation, and Uncertainty Quantification

Wilson, Eric; Parker, Andrew; Fontanini, Anthony D.; Present, Elaina; Reyna, Janet; Adhikari, Rajendra; Bianchi, Carlo; CaraDonna, Christopher; Dahlhausen, Matthew; Kim, Jang-Hyun; LeBar, Amy; Liu, Lixi; Praprost, Marlena; Zhang, Liang; DeWitt, Peter; Merket, Noel; Speake, Andrew; Hong, Tianzhen; Li, Han; Frick, Natalie Mims; Wang, Zhe; Blair, Aileen; Horsey, Ry; Roberts, David; Trenbath, Kim; Adekanye, Oluwatobi; Bonnema, Eric; Kontar, Rawad El; González, Jonathan; Horowitz, Scott; Jones, Dalton; Muehleisen, Ralph T.; Platthotam, Siby; Reynolds, Matthew R.; Robertson, Joseph; Sayers, Kevin; Li, Qu

doi:10.2172/1854582

Cited by 43 publications

(32 citation statements)

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“…As such, we will maintain an accompanying online dashboard-available at resstock.nrel.gov/page/typology-that provides the most up-to-date data as well as custom query capabilities down to a county level. The results we present here in this report are consistent with the state of ResStock used to produce the End-Use Load Profiles dataset v1.0; the output correction model discussed in the End-Use Load Profiles report has not been applied to these results (Wilson et al 2021).…”

Section: Development Of a Us Building Typologysupporting

confidence: 73%

“…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).…”

supporting

confidence: 72%

“…An initial ComStock validation was completed in 2019. ResStock and ComStock validation, with a focus on end-use load profiles, was completed under the DOE project End-Use Load Profiles for the U.S. Building Stock(Mims Frick et al 2019;Wilson et al 2021).…”

mentioning

confidence: 99%

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

Reyna¹,

Wilson²,

Parker³

et al. 2022

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This report, originally published in December 2021, has been revised in July 2022 to expand the report scope to include commercial characterization in addition to the residential characterization in the original publication. The majority of these changes are in the new Section 3.2. Additionally, to support this scope expansion, we have included the additional section authors, added two appendences, modified the executive summary and conclusions, and updated text throughout to be inclusive of both the residential and commercial sectors. iv This report is available at no cost from the National Renewable Energy Laboratory (NREL) at www.nrel.gov/publications.

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Section: Development Of a Us Building Typologysupporting

confidence: 73%

supporting

confidence: 72%

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

Reyna¹,

Wilson²,

Parker³

et al. 2022

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“…Although as of 2019, over 60% of all electric meters nationwide included advanced metering infrastructure (AMI), which collect hourly or sub-hourly electricity demand data, wide-scale hourly demand datasets are not publicly available due to privacy concerns [24,25]. However, the National Renewable Energy Laboratory (NREL) recently published a dataset of approximately 900 000 simulated end-use load profiles which have been calibrated and validated using actual meter data and statistically represent the US residential and commercial building stock [26,27]. Each of the 14 unique commercial building types and nine unique residential building types (summarized in the SI) are represented by individual building variants with different combinations of physical and operational characteristics that affect the load profile.…”

Section: Hourly Building Demand Datamentioning

confidence: 99%

Hourly accounting of carbon emissions from electricity consumption

Miller

Novan

Jenn

2022

Environ. Res. Lett.

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Carbon accounting is important for quantifying the sources of greenhouse gas (GHG) emissions that are driving climate change, and is increasingly being used to guide policy, investment, business, and regulatory decisions. The current practice for accounting emissions from consumed electricity, guided by standards like the GHG Protocol, uses annual-average grid emission factors, although previous studies have shown that grid carbon intensity varies across seasons and hours of the day. Previous case studies have shown that annual-average carbon accounting can bias emission inventories, but none have shown that this bias is substantial or widespread. This study addresses this gap by calculating emission inventories for thousands of residential, commercial, industrial, and agricultural facilities across the U.S., and explores the magnitude and direction of this bias compared to hourly accounting of emissions. Our results show that annual-average accounting can over- or under-estimate carbon inventories as much as 35% in certain settings but result in effectively no bias in others. Bias will be greater in regions with high variation in carbon intensity, and for end-users with high variation in their electricity consumption across hours and seasons. As variation in carbon intensity continues to grow with growing shares of variable and intermittent renewable generation, these biases will only continue to worsen in the future. In most cases, using monthly-average emission factors does not substantially reduce bias compared to annual averages. Thus, the authors recommend that hourly accounting be adopted as the best practice for emissions inventories of consumed electricity.

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“…Power plant operations, solar production, electrical transmission losses, and many other related systems have been studied and analyzed to set standards and benchmarks for how those systems should be run. Smaller subsystems on the grid have been studied as well, with extensive models, load profile shapes, and benchmarks for residential and commercial facilities 51,52 . The Environmental Protection Agency (EPA) is heavily involved with energy use benchmarking through their ENERGY STAR program.…”

Section: Five Improvement Areasmentioning

confidence: 99%

Grid‐responsive smart manufacturing: A perspective for an interconnected energy future in the industrial sector

Billings

Powell

2022

AIChE Journal

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With the growing amount of renewable energy sources, the grid has become responsible for accounting for intermittency and the flexibility needed to utilize dynamic sources. Expensive peaking plants and energy storage systems have been proposed as ways to mitigate those problems. There is a large group of energy consumers that can respond to grid conditions. Historically, these consumers have been residential and commercial users, but with modern innovations and practices, industrial consumers have the potential to become a major player in this space. Grid‐responsive smart manufacturing can be used to utilize modern tools in manufacturing innovation as enablers for grid response. These modern tools already exist but are not widely used for industrial grid‐side energy management. This article defines grid‐responsive smart manufacturing, identifies five major barriers to its widespread implementation, and portrays the path to getting industrial users to be key players in grid stability and flexibility.

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End-Use Load Profiles for the U.S. Building Stock: Methodology and Results of Model Calibration, Validation, and Uncertainty Quantification

Cited by 43 publications

References 0 publications

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

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

Hourly accounting of carbon emissions from electricity consumption

Grid‐responsive smart manufacturing: A perspective for an interconnected energy future in the industrial sector

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