IDEAS home Printed from https://ideas.repec.org/a/eee/energy/v238y2022ipas0360544221018855.html
   My bibliography  Save this article

Impact of stay home living on energy demand of residential buildings: Saudi Arabian case study

Author

Listed:
  • Aldubyan, Mohammad
  • Krarti, Moncef

Abstract

The analysis presented in this paper evaluates the impact of both short-term and long-term stay home living on the energy consumption of Saudi residential building stock. The analysis combines monitored data obtained for a sample of housing units as well as the results from a validated Saudi residential building stock model. In particular, a bottom-up building stock modeling analysis approach is used to estimate the energy impact of the 2020 stay home order imposed due to COVID-19 in most of Saudi Arabia's regions between March 15 and June 15, 2020. Moreover, the potential influences of long-term stay home patterns on the cost-benefits of energy retrofit programs are investigated targeting Saudi existing housing stocks. The analysis results indicate that when normalized to weather, the 2020 lockdown has resulted in a 16 % increase in electricity consumption compared to the 2019 level specific to the entire Saudi housing stock. Most of this increase is associated with the higher energy end-use of lighting, appliances, and air conditioning associated with increased occupancy levels during daytime hours. For a transition to long-term stay home living, the results of the analysis show that the energy consumption of the Saudi housing stock is estimated to increase by 13.5 % compared to the current occupancy pattern. Moreover, the analysis indicates that the cost-benefits for energy efficiency retrofits are enhanced with long-term stay home living.

Suggested Citation

  • 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).
    • Handle: RePEc:eee:energy:v:238:y:2022:i:pa:s0360544221018855
    DOI: 10.1016/j.energy.2021.121637
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0360544221018855
    Download Restriction: Full text for ScienceDirect subscribers only
    File URL: https://libkey.io/10.1016/j.energy.2021.121637?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. D’Oca, Simona & Hong, Tianzhen & Langevin, Jared, 2018. "The human dimensions of energy use in buildings: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 81(P1), pages 731-742.
    2. Swan, Lukas G. & Ugursal, V. Ismet, 2009. "Modeling of end-use energy consumption in the residential sector: A review of modeling techniques," Renewable and Sustainable Energy Reviews, Elsevier, vol. 13(8), pages 1819-1835, October.
    3. Nicholas Bloom & James Liang & John Roberts & Zhichun Jenny Ying, 2015. "Does Working from Home Work? Evidence from a Chinese Experiment," The Quarterly Journal of Economics, President and Fellows of Harvard College, vol. 130(1), pages 165-218.
    4. 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.
    5. Li, Wenliang & Zhou, Yuyu & Cetin, Kristen & Eom, Jiyong & Wang, Yu & Chen, Gang & Zhang, Xuesong, 2017. "Modeling urban building energy use: A review of modeling approaches and procedures," Energy, Elsevier, vol. 141(C), pages 2445-2457.
    6. Ballarini, Ilaria & Corgnati, Stefano Paolo & Corrado, Vincenzo, 2014. "Use of reference buildings to assess the energy saving potentials of the residential building stock: The experience of TABULA project," Energy Policy, Elsevier, vol. 68(C), pages 273-284.
    7. 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.
    8. Yun, Geun Young & Steemers, Koen, 2011. "Behavioural, physical and socio-economic factors in household cooling energy consumption," Applied Energy, Elsevier, vol. 88(6), pages 2191-2200, June.
    9. Chen, Yixing & Hong, Tianzhen & Piette, Mary Ann, 2017. "Automatic generation and simulation of urban building energy models based on city datasets for city-scale building retrofit analysis," Applied Energy, Elsevier, vol. 205(C), pages 323-335.
    10. 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).
    11. Krarti, Moncef & Aldubyan, Mohammad & Williams, Eric, 2020. "Residential building stock model for evaluating energy retrofit programs in Saudi Arabia," Energy, Elsevier, vol. 195(C).
    12. Krarti, Moncef, 2015. "Evaluation of large scale building energy efficiency retrofit program in Kuwait," Renewable and Sustainable Energy Reviews, Elsevier, vol. 50(C), pages 1069-1080.
    13. Filogamo, Luana & Peri, Giorgia & Rizzo, Gianfranco & Giaccone, Antonino, 2014. "On the classification of large residential buildings stocks by sample typologies for energy planning purposes," Applied Energy, Elsevier, vol. 135(C), pages 825-835.
    14. An, Jingjing & Yan, Da & Hong, Tianzhen & Sun, Kaiyu, 2017. "A novel stochastic modeling method to simulate cooling loads in residential districts," Applied Energy, Elsevier, vol. 206(C), pages 134-149.
    15. Krarti, Moncef & Dubey, Kankana & Howarth, Nicholas, 2017. "Evaluation of building energy efficiency investment options for the Kingdom of Saudi Arabia," Energy, Elsevier, vol. 134(C), pages 595-610.
    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. Ioana-Ancuta Iancu & Patrick Hendrick & Micu DDM Dan Doru & Adrian Cote, 2023. "Pandemic-Induced Shifts in Climate Change Perception and Energy Consumption Behaviors: A Cross-Country Analysis of Belgium, Italy, Romania, and Sweden," ULB Institutional Repository 2013/377982, ULB -- Universite Libre de Bruxelles.
    2. Hong, Yejin & Yoon, Sungmin & Choi, Sebin, 2023. "Operational signature-based symbolic hierarchical clustering for building energy, operation, and efficiency towards carbon neutrality," Energy, Elsevier, vol. 265(C).
    3. Beccali, Marco & Bonomolo, Marina & Martorana, Francesca & Catrini, Pietro & Buscemi, Alessandro, 2022. "Electrical hybrid heat pumps assisted by natural gas boilers: a review," Applied Energy, Elsevier, vol. 322(C).
    4. M. Mobeen Shaukat & Muhammad Luqman & Muhammad Asif & S. Z. Shuja & Mohammed Qannan, 2024. "A Theoretical Framework to Promote LCA in the Construction Industry of Saudi Arabia," Sustainability, MDPI, vol. 16(9), pages 1-13, April.
    5. Salma Hamad Almuhaini & Nahid Sultana, 2023. "Bayesian-Optimization-Based Long Short-Term Memory (LSTM) Super Learner Approach for Modeling Long-Term Electricity Consumption," Sustainability, MDPI, vol. 15(18), pages 1-23, September.
    6. Ioana Ancuta Iancu & Patrick Hendrick & Dan Doru Micu & Adrian Cote, 2023. "Pandemic-Induced Shifts in Climate Change Perception and Energy Consumption Behaviors: A Cross-Country Analysis of Belgium, Italy, Romania, and Sweden," Sustainability, MDPI, vol. 15(20), pages 1-24, October.

    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. 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).
    2. Yang, Xining & Hu, Mingming & Heeren, Niko & Zhang, Chunbo & Verhagen, Teun & Tukker, Arnold & Steubing, Bernhard, 2020. "A combined GIS-archetype approach to model residential space heating energy: A case study for the Netherlands including validation," Applied Energy, Elsevier, vol. 280(C).
    3. Yang, Xiu'e & Liu, Shuli & Zou, Yuliang & Ji, Wenjie & Zhang, Qunli & Ahmed, Abdullahi & Han, Xiaojing & Shen, Yongliang & Zhang, Shaoliang, 2022. "Energy-saving potential prediction models for large-scale building: A state-of-the-art review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 156(C).
    4. Oraiopoulos, A. & Howard, B., 2022. "On the accuracy of Urban Building Energy Modelling," Renewable and Sustainable Energy Reviews, Elsevier, vol. 158(C).
    5. Wang, Wei & Hong, Tianzhen & Xu, Xiaodong & Chen, Jiayu & Liu, Ziang & Xu, Ning, 2019. "Forecasting district-scale energy dynamics through integrating building network and long short-term memory learning algorithm," Applied Energy, Elsevier, vol. 248(C), pages 217-230.
    6. Martin Eriksson & Jan Akander & Bahram Moshfegh, 2022. "Investigating Energy Use in a City District in Nordic Climate Using Energy Signature," Energies, MDPI, vol. 15(5), pages 1-22, March.
    7. Wenliang Li, 2020. "Quantifying the Building Energy Dynamics of Manhattan, New York City, Using an Urban Building Energy Model and Localized Weather Data," Energies, MDPI, vol. 13(12), pages 1-22, June.
    8. Krarti, Moncef & Aldubyan, Mohammad & Williams, Eric, 2020. "Residential building stock model for evaluating energy retrofit programs in Saudi Arabia," Energy, Elsevier, vol. 195(C).
    9. Ozarisoy, B. & Altan, H., 2022. "Significance of occupancy patterns and habitual household adaptive behaviour on home-energy performance of post-war social-housing estate in the South-eastern Mediterranean climate: Energy policy desi," Energy, Elsevier, vol. 244(PB).
    10. Bischof, Julian & Duffy, Aidan, 2022. "Life-cycle assessment of non-domestic building stocks: A meta-analysis of current modelling methods," Renewable and Sustainable Energy Reviews, Elsevier, vol. 153(C).
    11. Shen, Pengyuan & Wang, Huilong, 2024. "Archetype building energy modeling approaches and applications: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 199(C).
    12. Krarti, Moncef & Aldubyan, Mohammad, 2021. "Mitigation analysis of water consumption for power generation and air conditioning of residential buildings: Case study of Saudi Arabia," Applied Energy, Elsevier, vol. 290(C).
    13. Solène Goy & François Maréchal & Donal Finn, 2020. "Data for Urban Scale Building Energy Modelling: Assessing Impacts and Overcoming Availability Challenges," Energies, MDPI, vol. 13(16), pages 1-23, August.
    14. Langevin, J. & Reyna, J.L. & Ebrahimigharehbaghi, S. & Sandberg, N. & Fennell, P. & Nägeli, C. & Laverge, J. & Delghust, M. & Mata, É. & Van Hove, M. & Webster, J. & Federico, F. & Jakob, M. & Camaras, 2020. "Developing a common approach for classifying building stock energy models," Renewable and Sustainable Energy Reviews, Elsevier, vol. 133(C).
    15. Abbasabadi, Narjes & Ashayeri, Mehdi & Azari, Rahman & Stephens, Brent & Heidarinejad, Mohammad, 2019. "An integrated data-driven framework for urban energy use modeling (UEUM)," Applied Energy, Elsevier, vol. 253(C), pages 1-1.
    16. Katal, Ali & Mortezazadeh, Mohammad & Wang, Liangzhu (Leon), 2019. "Modeling building resilience against extreme weather by integrated CityFFD and CityBEM simulations," Applied Energy, Elsevier, vol. 250(C), pages 1402-1417.
    17. Avichal Malhotra & Simon Raming & Jérôme Frisch & Christoph van Treeck, 2021. "Open-Source Tool for Transforming CityGML Levels of Detail," Energies, MDPI, vol. 14(24), pages 1-26, December.
    18. Ahmed, Wahhaj & Asif, Muhammad, 2021. "A critical review of energy retrofitting trends in residential buildings with particular focus on the GCC countries," Renewable and Sustainable Energy Reviews, Elsevier, vol. 144(C).
    19. Pasichnyi, Oleksii & Wallin, Jörgen & Kordas, Olga, 2019. "Data-driven building archetypes for urban building energy modelling," Energy, Elsevier, vol. 181(C), pages 360-377.
    20. Kristensen, Martin Heine & Hedegaard, Rasmus Elbæk & Petersen, Steffen, 2020. "Long-term forecasting of hourly district heating loads in urban areas using hierarchical archetype modeling," Energy, Elsevier, vol. 201(C).

    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:energy:v:238:y:2022:i:pa:s0360544221018855. 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.journals.elsevier.com/energy .

    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.