Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area
Several companies, universities, and national laboratories are developing urban-scale energy modeling that allows the creation of a digital twin of buildings for the simulation and optimization of real-world, city-sized areas. Prior to simulation-based assessment, a baseline of savings for a set of...
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Published in | Energies (Basel) Vol. 14; no. 1; p. 132 |
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Main Authors | , , |
Format | Journal Article |
Language | English |
Published |
Basel
MDPI AG
01.01.2021
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Abstract | Several companies, universities, and national laboratories are developing urban-scale energy modeling that allows the creation of a digital twin of buildings for the simulation and optimization of real-world, city-sized areas. Prior to simulation-based assessment, a baseline of savings for a set of utility-defined use cases was established to clarify the initial business case for specific energy efficient building technologies. In partnership with a municipal utility, 178,337 OpenStudio and EnergyPlus models of buildings in the utility’s 1400 km2 service area were created, simulated, and assessed with measures for quantifying energy, demand, cost, and emissions reductions of each building. The method of construction and assumptions behind these models is discussed, definitions of example measures are provided, and distribution of savings across the building stock is provided under a maximum technical adoption scenario. |
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AbstractList | Several companies, universities, and national laboratories are developing urban-scale energy modeling that allows the creation of a digital twin of buildings for the simulation and optimization of real-world, city-sized areas. Prior to simulation-based assessment, a baseline of savings for a set of utility-defined use cases was established to clarify the initial business case for specific energy efficient building technologies. In partnership with a municipal utility, 178,337 OpenStudio and EnergyPlus models of buildings in the utility’s 1400 km2 service area were created, simulated, and assessed with measures for quantifying energy, demand, cost, and emissions reductions of each building. The method of construction and assumptions behind these models is discussed, definitions of example measures are provided, and distribution of savings across the building stock is provided under a maximum technical adoption scenario. |
Author | Copeland, William Bass, Brett New, Joshua |
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BackLink | https://www.osti.gov/biblio/1752977$$D View this record in Osti.gov |
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CitedBy_id | crossref_primary_10_3390_buildings12112004 crossref_primary_10_1080_20964471_2022_2160156 crossref_primary_10_1109_ACCESS_2021_3065087 crossref_primary_10_3390_en15197002 crossref_primary_10_1016_j_enbuild_2023_113171 crossref_primary_10_3390_buildings12020113 |
Cites_doi | 10.1016/j.apenergy.2018.05.023 10.1007/s12273-016-0332-1 10.1016/j.buildenv.2017.10.029 10.1016/j.enbuild.2017.08.029 10.1016/j.energy.2016.10.057 10.1016/j.enbuild.2016.10.050 10.2172/1009264 10.1016/S0378-7788(00)00114-6 |
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Notes | USDOE Office of Electricity (OE) USDOE Office of Energy Efficiency and Renewable Energy (EERE), Energy Efficiency Office. Building Technologies Office AC05-00OR22725 |
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SubjectTerms | Buildings Commercial energy Construction Cost control Customer services Decision making demand side management Electric rates Electricity Electricity distribution Emissions Energy Energy conservation ENERGY CONSERVATION, CONSUMPTION, AND UTILIZATION Energy consumption Energy efficiency Energy management Energy modeling grid-interactive efficient buildings Industrial plant emissions Optimization Potential energy Power Service areas Simulation urban-scale energy modeling valuation |
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Title | Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area |
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