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.
Highlights
In 2019, approximately 125 million buildings in the United States accounted for USD 412 billion in energy bills, 40% of national energy consumption, 73% of electrical consumption, 80% of peak demand, and 39% of emissions
Building energy modeling at an urban scale is primarily carried out using a bottom-up approach as individual building simulations allow for more specific results and decision making
The methodology for collecting data that are used is among the primary factors in distinguishing Urban building energy modeling (UBEM) approaches as more data used to describe a building often lead to more accurate results
Summary
In 2019, approximately 125 million buildings in the United States accounted for USD 412 billion in energy bills, 40% of national energy consumption, 73% of electrical consumption, 80% of peak demand, and 39% of emissions. The current combination results in an overall set of 2448 building models that covers 80% of the US commercial floorspace [8] These models have been used to analyze the energy savings and cost impacts of energy-efficiency code updates [9,10]; develop prescriptive new construction and retrofit design guides [11,12]; create technical potential scales for building asset scores [13,14]; develop typical energy-conservation measure savings estimates for up-front incentives through utility programs [15]; create performance, cost, lifetime and time-to-market targets for new technologies to inform DOE’s technology investment portfolio [16]; and many other applications [17,18,19,20,21]. The range of acceptable error rates is often specific to a certain use case and can lead to objections for use in building codes, informing standards development, utility program rollout, energy efficiency investments, or other decision-making criteria
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