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Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area

Author

Listed:
  • Brett Bass

    (Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • Joshua New

    (Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

  • William Copeland

    (Oak Ridge National Laboratory, Oak Ridge, TN 37831, USA)

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 km 2 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.

Suggested Citation

  • Brett Bass & Joshua New & William Copeland, 2020. "Potential Energy, Demand, Emissions, and Cost Savings Distributions for Buildings in a Utility’s Service Area," Energies, MDPI, vol. 14(1), pages 1-23, December.
    • Handle: RePEc:gam:jeners:v:14:y:2020:i:1:p:132-:d:469856
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    References listed on IDEAS

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    1. Cerezo Davila, Carlos & Reinhart, Christoph F. & Bemis, Jamie L., 2016. "Modeling Boston: A workflow for the efficient generation and maintenance of urban building energy models from existing geospatial datasets," Energy, Elsevier, vol. 117(P1), pages 237-250.
    2. Nutkiewicz, Alex & Yang, Zheng & Jain, Rishee K., 2018. "Data-driven Urban Energy Simulation (DUE-S): A framework for integrating engineering simulation and machine learning methods in a multi-scale urban energy modeling workflow," Applied Energy, Elsevier, vol. 225(C), pages 1176-1189.
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