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Research Article

Evaluating the energy impact potential of energy efficiency measures for retrofit applications: A case study with U.S. medium office buildings

Yunyang Ye1Kathryn Hinkelman2Yingli Lou2Wangda Zuo2,3( )Gang Wang4Jian Zhang1
Pacific Northwest National Laboratory, Richland, WA 99354, USA
Department of Civil, Environmental and Architectural Engineering, University of Colorado Boulder, Boulder, CO 80309, USA
National Renewable Energy Laboratory, Golden, CO 80401, USA
Civil, Architectural and Environmental Engineering Department, University of Miami, Coral Gables, FL 33146, USA
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Abstract

Quantifying the energy savings of various energy efficiency measures (EEMs) for an energy retrofit project often necessitates an energy audit and detailed whole building energy modeling to evaluate the EEMs; however, this is often cost-prohibitive for small and medium buildings. In order to provide a defined guideline for projects with assumed common baseline characteristics, this paper applies a sensitivity analysis method to evaluate the impact of individual EEMs and groups these into packages to produce deep energy savings for a sample prototype medium office building across 15 climate zones in the United States. We start with one baseline model for each climate zone and nine candidate EEMs with a range of efficiency levels for each EEM. Three energy performance indicators (EPIs) are defined, which are annual electricity use intensity, annual natural gas use intensity, and annual energy cost. Then, a Standard Regression Coefficient (SRC) sensitivity analysis method is applied to determine the sensitivity of each EEM with respect to the three EPIs, and the relative sensitivity of all EEMs are calculated to evaluate their energy impacts. For the selected range of efficiency levels, the results indicate that the EEMs with higher energy impacts (i.e., higher sensitivity) in most climate zones are high-performance windows, reduced interior lighting power, and reduced interior plug and process loads. However, the sensitivity of the EEMs also vary by climate zone and EPI; for example, improved opaque envelope insulation and efficiency of cooling and heating systems are found to have a high energy impact in cold and hot climates.

References

 
Albadi MH, El-Saadany EF (2007). Demand response in electricity markets: An overview. In: Proceedings of 2007 IEEE Power Engineering Society General Meeting, Tampa, FL, USA.
 
ASHRAE (2004). ASHRAE Standard 90.1-2004, Energy Standard for Buildings Except Low Rise Residential Buildings. Atlanta, GA, USA: The American Society of Heating, Refrigerating and Air-Conditioning Engineers.
 
ASHRAE (2007). ASHRAE Standard 90.1-2007, Energy Standard for Buildings Except Low Rise Residential Buildings. Atlanta, GA, USA: The American Society of Heating, Refrigerating and Air-Conditioning Engineers.
 
ASHRAE (2010). ASHRAE Standard 90.1-2010, Energy Standard for Buildings Except Low Rise Residential Buildings. Atlanta, GA, USA: The American Society of Heating, Refrigerating and Air-Conditioning Engineers.
 
Bonnema E, Leach M, Pless S, et al. (2012). 50% Advanced Energy Design Guides. Golden, CO, USA: National Renewable Energy Laboratoy (NREL).
 
Breesch H, Janssens A (2010). Performance evaluation of passive cooling in office buildings based on uncertainty and sensitivity analysis. Solar Energy, 84:1453-1467.
 
Corrado V, Mechri HE (2009). Uncertainty and sensitivity analysis for building energy rating. Journal of Building Physics, 33: 125-156.
 
Delgarm N, Sajadi B, Azarbad K, et al. (2018). Sensitivity analysis of building energy performance: a simulation-based approach using OFAT and variance-based sensitivity analysis methods. Journal of Building Engineering, 15: 181-193.
 
DOE (2011). Commercial Reference Building Models. Available at https://energy.gov/eere/buildings/commercial-reference-buildings.
 
DOE (2017). EnergyPlus. Available at https://energyplus.net/.
 
DOE (2018). Status of State Energy Code Adoption. Available at https://www.energycodes.gov/status-state-energy-code-adoption.
 
DOE (2020). Commercial Prototype Building Models. Available at https://www.energycodes.gov/commercial-prototype-building-models.
 
Doostizadeh M, Ghasemi H (2012). A day-ahead electricity pricing model based on smart metering and demand-side management. Energy, 46: 221-230.
 
EIA (2012). Commercial Buildings Energy Consumption Survey. Available at https://www.eia.gov/consumption/commercial/data/2012/.
 
Eisenhower B, O’Neill Z, Fonoberov VA, Mezić I (2012). Uncertainty and sensitivity decomposition of building energy models. Journal of Building Performance Simulation, 5: 171-184.
 
Glazer J (2016). Development of maximum technically achievable energy targets for commercial buildings. ASHRAE Transactions, 123(2): 32-52.
 
Gordian (2020). Construction Cost Estimating Software: Rsmeans Data. Available at https://www.rsmeans.com.
 
Griffith B, Long N, Torcellini P, et al. (2007). Assessment of the technical potential for achieving net zero-energy buildings in the commercial sector. Golden, CO, USA: National Renewable Energy Laboratoy (NREL),
 
Heo Y, Choudhary R, Augenbroe GA (2012). Calibration of building energy models for retrofit analysis under uncertainty. Energy and Buildings, 47: 550-560.
 
Hygh JS, DeCarolis JF, Hill DB, et al. (2012). Multivariate regression as an energy assessment tool in early building design. Building and Environment, 57: 165-175.
 
Iooss B, Lemaître P (2015). A review on global sensitivity analysis methods. In: Dellino G, Meloni C (eds), Uncertainty Management in Simulation-Optimization of Complex Systems. Boston, MA, USA: Springer.
 
Joskow PL, Wolfram CD (2012). Dynamic pricing of electricity. American Economic Review, 102: 381-385.
 
Kneifel J (2010). Life-cycle carbon and cost analysis of energy efficiency measures in new commercial buildings. Energy and Buildings, 42: 333-340.
 
Li H, Wang S, Cheung H (2018). Sensitivity analysis of design parameters and optimal design for zero/low energy buildings in subtropical regions. Applied Energy, 228: 1280-1291.
 
Liu G, Liu B, Wang W et al. (2011a). Advanced energy retrofit guide office buildings. Richland, WA, USA: Pacific Northwest National Laboratoy (PNNL).
 
Liu G, Liu B, Wang W et al. (2011b). Advanced energy retrofit guide retail buildings. Richland, WA, USA: Pacific Northwest National Laboratoy (PNNL).
 
Menberg K, Heo Y, Choudhary R (2016). Sensitivity analysis methods for building energy models: Comparing computational costs and extractable information. Energy and Buildings, 133: 433-445.
 
Mokhtari A, Frey HC (2005). Review and recommendation of methods for sensitivity and uncertainty analysis for the Stochastic Human Exposure and Dose Simulation (SHEDS) models: Volume II: Evaluation and recommendation of methodology for conducting sensitivity analysis in probabilistic models. Durham, NC, USA: Alion Science and Technology.
 
Moser D, Liu G, Wang W, et al. (2012). Achieving deep energy savings in existing buildings through integrated design. ASHRAE Transactions, 118(2): 3-10.
 
NBI (2013). New Construction Guide: A prescriptive guide to achieve significant, predictable energy savings in new commercial buildings. Vancouver, WA, USA: New Buildings Institute (NBI).
 
Nguyen AT, Reiter S (2015). A performance comparison of sensitivity analysis methods for building energy models. Building Simulation, 8: 651-664.
 
NREL (2018). OpenStudio-Standards Gem. Available at https://github.com/NREL/openstudio-standards.
 
Pang Z, O’Neill Z (2018). Uncertainty quantification and sensitivity analysis of the domestic hot water usage in hotels. Applied Energy, 232: 424-442.
 
Qiu S, Li Z, Pang Z, et al. (2018). A quick auto-calibration approach based on normative energy models. Energy and Buildings, 172: 35-46.
 
Sanchez DG, Lacarrière B, Musy M, et al. (2014). Application of sensitivity analysis in building energy simulations: Combining first-and second-order elementary effects methods. Energy and Buildings, 68: 741-750.
 
Spitz C, Mora L, Wurtz E, et al. (2012). Practical application of uncertainty analysis and sensitivity analysis on an experimental house. Energy and Buildings, 55: 459-470.
 
Stadler M, Kloess M, Groissböck M, et al. (2013). Electric storage in California's commercial buildings. Applied Energy, 104: 711-722.
 
Stein M (1987). Large sample properties of simulations using Latin hypercube sampling. Technometrics, 29: 143-151.
 
Storlie CB, Helton JC (2008). Multiple predictor smoothing methods for sensitivity analysis: Description of techniques. Reliability Engineering & System Safety, 93: 28-54.
 
Thornton B, Rosenberg M, Richman E, et al. (2011). Achieving the 30% goal: Energy and cost savings analysis of ASHRAE Standard 90.1-2010. Richland, WA, USA: Pacific Northwest National Laboratoy (PNNL).
 
Tian W (2013). A review of sensitivity analysis methods in building energy analysis. Renewable and Sustainable Energy Reviews, 20: 411-419.
 
Tian W, Song J, Li Z, et al. (2014). Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis. Applied Energy, 135: 320-328.
 
Tian W, de Wilde P, Li Z, et al. (2018). Uncertainty and sensitivity analysis of energy assessment for office buildings based on Dempster-Shafer theory. Energy Conversion and Management, 174: 705-718.
 
Wang W, Zhang J, Moser D, et al. (2012). Energy and cost savings of retro-commissioning and retrofit measures for large office buildings. In: In: Proceedings of the 5th National Conference of IBPSA-USA (SimBuild 2012), Madison, WI, USA.
 
Wang N, Goel S, Makhmalbaf A (2013). Commercial building energy asset score system: program overview and technical protocol (version 1.1). Richland, WA, USA: Pacific Northwest National Laboratoy (PNNL).
 
Wang N, Goel S, Makhmalbaf A, et al. (2015). Building energy asset score program overview and technical protocol (version 1.2). Richland, WA, USA: Pacific Northwest National Laboratoy (PNNL).
 
Wang Z, Zhao J (2018). Optimization of passive envelop energy efficient measures for office buildings in different climate regions of China based on modified sensitivity analysis. Sustainability, 10: 907.
 
Wang N, Goel S, Makhmalbaf A, et al. (2018). Development of building energy asset rating using stock modelling in the USA. Journal of Building Performance Simulation, 11: 4-18.
 
Ye Y, Wang G, Zuo W (2018a). Creation of a Prototype Building Model of College and University Building. In: Proceedings of the 4th International Conference on Building Energy and Environment (COBEE2018), Melbourne, Australia.
 
Ye Y, Wang G, Zuo W, et al. (2018b). Development of a baseline building model of auto service and repair shop. In: Proceedings of 2018 ASHRAE Building Performance Analysis Conference and SimBuild (BPACS2018), Chicago, IL, USA.
 
Ye Y, Hinkelman K, Zhang J, et al. (2019). A methodology to create prototypical building energy models for existing buildings: A case study on US religious worship buildings. Energy and Buildings, 194: 351-365.
Building Simulation
Pages 1377-1393
Cite this article:
Ye Y, Hinkelman K, Lou Y, et al. Evaluating the energy impact potential of energy efficiency measures for retrofit applications: A case study with U.S. medium office buildings. Building Simulation, 2021, 14(5): 1377-1393. https://doi.org/10.1007/s12273-021-0765-z

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Received: 02 August 2020
Accepted: 04 January 2021
Published: 01 March 2021
© Tsinghua University Press and Springer-Verlag GmbH Germany, part of Springer Nature 2021
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