IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v223y2018icp159-171.html
   My bibliography  Save this article

Estimating the influence of occupant behavior on building heating and cooling energy in one simulation run

Author

Listed:
  • Gaetani, Isabella
  • Hoes, Pieter-Jan
  • Hensen, Jan L.M.

Abstract

Energy performance contracting (EPC) aims at guaranteeing a specified level of energy savings in the built environment for a client. Among the building energy performance uncertainties that hinder EPC, occupant behavior (OB) plays a major role. For this reason, energy service companies (ESCOs) may be interested in including OB-related clauses in their contracts. The inclusion of such a clause calls for an efficient, easy-to-implement method to provide a first estimate of the potential effect of various aspects of OB on building cooling and heating energy demand. In contrast with common sensitivity analysis approaches based on a high number of scenarios, a novel simulation method requiring only a single simulation run for both heating and cooling seasons is presented here. The estimate is provided by evaluating the newly developed impact indices (II) based on the results obtained by means of the simulation run. A set of 16 building variants differing in floor height, climate, construction vintage and equipment and lighting power density was investigated to test the method. All II were calculated for the 16 building variants. In order to verify their significance, the results of a one-at-a-time sensitivity analysis mimicking simplified variations in occupant behavior (OB) were plotted against the II. The R2 values were above 0.9 when evaluating the effect of equipment use, lights use, and occupant presence, confirming the significance of the developed II. For blind use and temperature setpoint setting, the R2 values were ca. 0.85. Subsequently, the method was applied to an existing office building in Delft, The Netherlands, to evaluate its potential for EPC. This study confirms the high variability of the effect of OB on heating and cooling energy demand according to the case at hand. The developed method is useful for practitioners to evaluate the potential effect of OB on a given design in a time-effective manner.

Suggested Citation

  • Gaetani, Isabella & Hoes, Pieter-Jan & Hensen, Jan L.M., 2018. "Estimating the influence of occupant behavior on building heating and cooling energy in one simulation run," Applied Energy, Elsevier, vol. 223(C), pages 159-171.
  • Handle: RePEc:eee:appene:v:223:y:2018:i:c:p:159-171
    DOI: 10.1016/j.apenergy.2018.03.108
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S030626191830446X
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2018.03.108?utm_source=ideas
    LibKey link: if access is restricted and if your library uses this service, LibKey will redirect you to where you can use your library subscription to access this item
    ---><---

    As the access to this document is restricted, you may want to search for a different version of it.

    References listed on IDEAS

    as
    1. Kazmi, H. & D’Oca, S. & Delmastro, C. & Lodeweyckx, S. & Corgnati, S.P., 2016. "Generalizable occupant-driven optimization model for domestic hot water production in NZEB," Applied Energy, Elsevier, vol. 175(C), pages 1-15.
    2. Ghahramani, Ali & Zhang, Kenan & Dutta, Kanu & Yang, Zheng & Becerik-Gerber, Burcin, 2016. "Energy savings from temperature setpoints and deadband: Quantifying the influence of building and system properties on savings," Applied Energy, Elsevier, vol. 165(C), pages 930-942.
    3. Peng, Yuzhen & Rysanek, Adam & Nagy, Zoltán & Schlüter, Arno, 2018. "Using machine learning techniques for occupancy-prediction-based cooling control in office buildings," Applied Energy, Elsevier, vol. 211(C), pages 1343-1358.
    4. Kim, Jimin & Hong, Taehoon & Jeong, Jaemin & Lee, Myeonghwi & Lee, Minhyun & Jeong, Kwangbok & Koo, Choongwan & Jeong, Jaewook, 2017. "Establishment of an optimal occupant behavior considering the energy consumption and indoor environmental quality by region," Applied Energy, Elsevier, vol. 204(C), pages 1431-1443.
    5. Lin, Hung-Wen & Hong, Tianzhen, 2013. "On variations of space-heating energy use in office buildings," Applied Energy, Elsevier, vol. 111(C), pages 515-528.
    Full references (including those not matched with items on IDEAS)

    Citations

    Citations are extracted by the CitEc Project, subscribe to its RSS feed for this item.
    as


    Cited by:

    1. Gianmarco Fajilla & Marilena De Simone & Luisa F. Cabeza & Luís Bragança, 2020. "Assessment of the Impact of Occupants’ Behavior and Climate Change on Heating and Cooling Energy Needs of Buildings," Energies, MDPI, vol. 13(23), pages 1-18, December.
    2. Yan, Biao & Yang, Wansheng & He, Fuquan & Zeng, Wenhao, 2023. "Occupant behavior impact in buildings and the artificial intelligence-based techniques and data-driven approach solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    3. Ji Li & Wei Xu & Ping Cui & Biao Qiao & Siyang Wu & Chenghua Zhao, 2019. "Research on a Systematical Design Method for Nearly Zero-Energy Buildings," Sustainability, MDPI, vol. 11(24), pages 1-18, December.
    4. Mariola E. Zalewska, 2021. "The Impact of Incentives on Employees to Change Thermostat Settings—A Field Study," Energies, MDPI, vol. 14(17), pages 1-14, August.
    5. Niemierko, Rochus & Töppel, Jannick & Tränkler, Timm, 2019. "A D-vine copula quantile regression approach for the prediction of residential heating energy consumption based on historical data," Applied Energy, Elsevier, vol. 233, pages 691-708.
    6. Aldubyan, Mohammad & Krarti, Moncef, 2022. "Impact of stay home living on energy demand of residential buildings: Saudi Arabian case study," Energy, Elsevier, vol. 238(PA).
    7. Michele Roccotelli & Alessandro Rinaldi & Maria Pia Fanti & Francesco Iannone, 2020. "Building Energy Management for Passive Cooling Based on Stochastic Occupants Behavior Evaluation," Energies, MDPI, vol. 14(1), pages 1-24, December.
    8. Habtamu Tkubet Ebuy & Hind Bril El Haouzi & Riad Benelmir & Remi Pannequin, 2023. "Occupant Behavior Impact on Building Sustainability Performance: A Literature Review," Sustainability, MDPI, vol. 15(3), pages 1-23, January.
    9. Bianchi, Carlo & Zhang, Liang & Goldwasser, David & Parker, Andrew & Horsey, Henry, 2020. "Modeling occupancy-driven building loads for large and diversified building stocks through the use of parametric schedules," Applied Energy, Elsevier, vol. 276(C).
    10. Walker, Linus & Hischier, Illias & Schlueter, Arno, 2022. "Scenario-based robustness assessment of building system life cycle performance," Applied Energy, Elsevier, vol. 311(C).
    11. Uddin, Mohammad Nyme & Chi, Hung-Lin & Wei, His-Hsien & Lee, Minhyun & Ni, Meng, 2022. "Influence of interior layouts on occupant energy-saving behaviour in buildings: An integrated approach using Agent-Based Modelling, System Dynamics and Building Information Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    12. Xie, Jiantong & Pan, Yiqun & Jia, Wenqi & Xu, Lei & Huang, Zhizhong, 2019. "Energy-consumption simulation of a distributed air-conditioning system integrated with occupant behavior," Applied Energy, Elsevier, vol. 256(C).

    Most related items

    These are the items that most often cite the same works as this one and are cited by the same works as this one.
    1. Yan, Biao & Yang, Wansheng & He, Fuquan & Zeng, Wenhao, 2023. "Occupant behavior impact in buildings and the artificial intelligence-based techniques and data-driven approach solutions," Renewable and Sustainable Energy Reviews, Elsevier, vol. 184(C).
    2. Jing, Gang & Cai, Wenjian & Zhang, Xin & Cui, Can & Yin, Xiaohong & Xian, Huacai, 2019. "An energy-saving oriented air balancing strategy for multi-zone demand-controlled ventilation system," Energy, Elsevier, vol. 172(C), pages 1053-1065.
    3. Panagiotis Michailidis & Iakovos Michailidis & Dimitrios Vamvakas & Elias Kosmatopoulos, 2023. "Model-Free HVAC Control in Buildings: A Review," Energies, MDPI, vol. 16(20), pages 1-45, October.
    4. Tian, Wei & Song, Jitian & Li, Zhanyong & de Wilde, Pieter, 2014. "Bootstrap techniques for sensitivity analysis and model selection in building thermal performance analysis," Applied Energy, Elsevier, vol. 135(C), pages 320-328.
    5. Imran & Naeem Iqbal & Shabir Ahmad & Do Hyeun Kim, 2021. "Towards Mountain Fire Safety Using Fire Spread Predictive Analytics and Mountain Fire Containment in IoT Environment," Sustainability, MDPI, vol. 13(5), pages 1-23, February.
    6. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    7. Lee, Junghun & Kim, Jeonggook & Song, Doosam & Kim, Jonghun & Jang, Cheolyong, 2017. "Impact of external insulation and internal thermal density upon energy consumption of buildings in a temperate climate with four distinct seasons," Renewable and Sustainable Energy Reviews, Elsevier, vol. 75(C), pages 1081-1088.
    8. Jing Zhao & Yu Shan, 2020. "A Fuzzy Control Strategy Using the Load Forecast for Air Conditioning System," Energies, MDPI, vol. 13(3), pages 1-17, January.
    9. Ghahramani, Ali & Pantelic, Jovan & Lindberg, Casey & Mehl, Matthias & Srinivasan, Karthik & Gilligan, Brian & Arens, Edward, 2018. "Learning occupants’ workplace interactions from wearable and stationary ambient sensing systems," Applied Energy, Elsevier, vol. 230(C), pages 42-51.
    10. Glasgo, Brock & Hendrickson, Chris & Azevedo, Inês Lima, 2017. "Assessing the value of information in residential building simulation: Comparing simulated and actual building loads at the circuit level," Applied Energy, Elsevier, vol. 203(C), pages 348-363.
    11. Ruparathna, Rajeev & Hewage, Kasun & Sadiq, Rehan, 2016. "Improving the energy efficiency of the existing building stock: A critical review of commercial and institutional buildings," Renewable and Sustainable Energy Reviews, Elsevier, vol. 53(C), pages 1032-1045.
    12. Antonio Paone & Jean-Philippe Bacher, 2018. "The Impact of Building Occupant Behavior on Energy Efficiency and Methods to Influence It: A Review of the State of the Art," Energies, MDPI, vol. 11(4), pages 1-19, April.
    13. Jianwu Xiong & Linlin Chen & Yin Zhang, 2023. "Building Energy Saving for Indoor Cooling and Heating: Mechanism and Comparison on Temperature Difference," Sustainability, MDPI, vol. 15(14), pages 1-20, July.
    14. Pinto, Giuseppe & Deltetto, Davide & Capozzoli, Alfonso, 2021. "Data-driven district energy management with surrogate models and deep reinforcement learning," Applied Energy, Elsevier, vol. 304(C).
    15. Azar, Elie & Nikolopoulou, Christina & Papadopoulos, Sokratis, 2016. "Integrating and optimizing metrics of sustainable building performance using human-focused agent-based modeling," Applied Energy, Elsevier, vol. 183(C), pages 926-937.
    16. Afroz, Zakia & Urmee, Tania & Shafiullah, G.M. & Higgins, Gary, 2018. "Real-time prediction model for indoor temperature in a commercial building," Applied Energy, Elsevier, vol. 231(C), pages 29-53.
    17. Maltais, Louis-Gabriel & Gosselin, Louis, 2021. "Predictability analysis of domestic hot water consumption with neural networks: From single units to large residential buildings," Energy, Elsevier, vol. 229(C).
    18. Romero Rodríguez, Laura & Sánchez Ramos, José & Álvarez Domínguez, Servando & Eicker, Ursula, 2018. "Contributions of heat pumps to demand response: A case study of a plus-energy dwelling," Applied Energy, Elsevier, vol. 214(C), pages 191-204.
    19. Zhao, Haitao & Ezeh, Collins I. & Ren, Weijia & Li, Wentao & Pang, Cheng Heng & Zheng, Chenghang & Gao, Xiang & Wu, Tao, 2019. "Integration of machine learning approaches for accelerated discovery of transition-metal dichalcogenides as Hg0 sensing materials," Applied Energy, Elsevier, vol. 254(C).
    20. Gohar Gholamibozanjani & Mohammed Farid, 2021. "A Critical Review on the Control Strategies Applied to PCM-Enhanced Buildings," Energies, MDPI, vol. 14(7), pages 1-39, March.

    Corrections

    All material on this site has been provided by the respective publishers and authors. You can help correct errors and omissions. When requesting a correction, please mention this item's handle: RePEc:eee:appene:v:223:y:2018:i:c:p:159-171. See general information about how to correct material in RePEc.

    If you have authored this item and are not yet registered with RePEc, we encourage you to do it here. This allows to link your profile to this item. It also allows you to accept potential citations to this item that we are uncertain about.

    If CitEc recognized a bibliographic reference but did not link an item in RePEc to it, you can help with this form .

    If you know of missing items citing this one, you can help us creating those links by adding the relevant references in the same way as above, for each refering item. If you are a registered author of this item, you may also want to check the "citations" tab in your RePEc Author Service profile, as there may be some citations waiting for confirmation.

    For technical questions regarding this item, or to correct its authors, title, abstract, bibliographic or download information, contact: Catherine Liu (email available below). General contact details of provider: http://www.elsevier.com/wps/find/journaldescription.cws_home/405891/description#description .

    Please note that corrections may take a couple of weeks to filter through the various RePEc services.

    IDEAS is a RePEc service. RePEc uses bibliographic data supplied by the respective publishers.