Renames #1547
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Renames #1547
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joseph-robertson
commented
Jan 5, 2026
| Once the appropriate Markov chain model is picked for an occupant, the schedule generation proceeds with sampling of the starting activity at midnight at the beginning of the weather year and sampling of activity at each time step based on the transition probability given the activity of the previous time step using the Markov-chain transition probability matrix. This process repeats until the full-year schedule is generated for each occupant in the household. Next, the occupant schedules are split into end uses and then merged as a household. The occupant schedules are combined for activities with shareable appliances (e.g., two or more occupants cooking at the same time is one cooking event) and aggregated for individualized activities (e.g., personal hygiene for each occupant is added together for hot water fixtures). While each occupant can only engage in one activity at a time, the activities can overlap after aggregating to the household level. | ||
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| Next, the generator converts the household activity schedules into appliance power and hot water schedules. For laundry machines, dishwashers, and range ovens, the generator uses the activity schedules for onset only and samples separately for the duration and power consumption of the appliance, which comes from the 2011 Residential Building Stock Assessment Metering Study (RBSAM) by NEEA. For laundry, the dryer is modeled to start immediately after the washer. For appliance hot water, the activity schedules similarly provide the draw onset while the duration and flow rate are sampled using NREL's Domestic Hot Water Event Schedule Generator~\citep{Hendron2010}. In this way, the hot water schedule and power schedule for the clothes washer and dishwasher are only aligned in terms of the onset and not necessarily the duration. This is consistent with real hot water appliance cycles in which hot water is drawn typically at the beginning. Once an appliance cycle completes with a minimum time gap, the generator finds the next activity onset from the activity schedules and the process repeats until all appliance schedules are created. | ||
| Next, the generator converts the household activity schedules into appliance power and hot water schedules. For laundry machines, dishwashers, and range ovens, the generator uses the activity schedules for onset only and samples separately for the duration and power consumption of the appliance, which comes from the 2011 Residential Building Stock Assessment Metering Study (RBSAM) by NEEA. For laundry, the dryer is modeled to start immediately after the washer. For appliance hot water, the activity schedules similarly provide the draw onset while the duration and flow rate are sampled using NLR's Domestic Hot Water Event Schedule Generator~\citep{Hendron2010}. In this way, the hot water schedule and power schedule for the clothes washer and dishwasher are only aligned in terms of the onset and not necessarily the duration. This is consistent with real hot water appliance cycles in which hot water is drawn typically at the beginning. Once an appliance cycle completes with a minimum time gap, the generator finds the next activity onset from the activity schedules and the process repeats until all appliance schedules are created. |
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joseph-robertson
commented
Jan 5, 2026
| Since initial development over a decade ago, ResStock has had dozens of researchers contribute to the structure, features, theory, and publication of data. In particular, we'd like to acknowledge Craig Christensen, who was instrumental in the initial model development. Additionally, we'd like to acknowledge our peer reviewers on this document: Andrew Parker and Jon Winkler. Additionally, we'd like to acknowledge U.S. Department of Energy (DOE) staff who have supported and guided ResStock development, including Dale Hoffmeyer, Amir Roth, Gretchen Maia, Asa Foss, Amy Royden-Bloom, and Eric Werling of the Building Technologies Office; Joan Glickman of the Office of State and Community Energy Programs; John Agan, Jenah Zweig, and Erin Boyd of the Office of Policy; and Robert Weber of the Bonneville Power Administration. Additionally, ResStock has been improved upon through work for parties outside of DOE, most notably the Los Angeles Department of Water and Power. We also would like to acknowledge the work of the EnergyPlus\textsuperscript{\textregistered} whole-building energy modeling tool, the OpenStudio\textsuperscript{\textregistered} SDK, and the OpenStudio-HPXML schema implementation, which provide the foundational model underpinnings of energy simulation in ResStock and which are the result of years of hard work by many people across DOE, the national laboratories, and the private sector. | ||
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| This work was authored by the National Renewable Energy Laboratory, operated by Alliance for Sustainable Energy, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Energy Efficiency and Renewable Energy Building Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. | ||
| This work was authored by the National Laboratory of the Rockies, operated by Alliance for Energy Innovation, LLC, for the U.S. Department of Energy (DOE) under Contract No. DE-AC36-08GO28308. Funding provided by U.S. Department of Energy Office of Critical Minerals and Energy Innovation Building Technologies Office. The views expressed in the article do not necessarily represent the views of the DOE or the U.S. Government. |
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Pull Request Description
NREL to NLR, Sustainable Energy to Energy Innovation.
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