IDEAS home Printed from https://ideas.repec.org/a/eee/appene/v160y2015icp431-441.html
   My bibliography  Save this article

The Solar Response Factor to calculate the cooling load induced by solar gains

Author

Listed:
  • Evola, G.
  • Marletta, L.

Abstract

This paper introduces an original approach for the evaluation of the cooling load due to the solar radiation incident on the glazed surface of a building. This approach is based on a newly introduced parameter called Solar Response Factor, defined as the overall convective heat flux released by the building envelope to the indoor space per unit radiant heat flux acting on the outer surface of the glazing.

Suggested Citation

  • Evola, G. & Marletta, L., 2015. "The Solar Response Factor to calculate the cooling load induced by solar gains," Applied Energy, Elsevier, vol. 160(C), pages 431-441.
  • Handle: RePEc:eee:appene:v:160:y:2015:i:c:p:431-441
    DOI: 10.1016/j.apenergy.2015.09.072
    as

    Download full text from publisher

    File URL: http://www.sciencedirect.com/science/article/pii/S0306261915011812
    Download Restriction: Full text for ScienceDirect subscribers only

    File URL: https://libkey.io/10.1016/j.apenergy.2015.09.072?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. Antonopoulos, K.A. & Gioti, F. & Tzivanidis, C., 2010. "A transient model for the energy analysis of indoor spaces," Applied Energy, Elsevier, vol. 87(10), pages 3084-3091, October.
    2. Buonomano, Annamaria & Palombo, Adolfo, 2014. "Building energy performance analysis by an in-house developed dynamic simulation code: An investigation for different case studies," Applied Energy, Elsevier, vol. 113(C), pages 788-807.
    3. Lv, Liugen & Huang, Chen & Li, Li & Chen, Jianchang, 2015. "Experimental study on calculation method of the radiant time factors," Renewable Energy, Elsevier, vol. 73(C), pages 28-35.
    4. Tzivanidis, C. & Antonopoulos, K.A. & Gioti, F., 2011. "Numerical simulation of cooling energy consumption in connection with thermostat operation mode and comfort requirements for the Athens buildings," Applied Energy, Elsevier, vol. 88(8), pages 2871-2884, August.
    5. Foucquier, Aurélie & Robert, Sylvain & Suard, Frédéric & Stéphan, Louis & Jay, Arnaud, 2013. "State of the art in building modelling and energy performances prediction: A review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 23(C), pages 272-288.
    6. Seo, Dong-yeon & Koo, Choongwan & Hong, Taehoon, 2015. "A Lagrangian finite element model for estimating the heating and cooling demand of a residential building with a different envelope design," Applied Energy, Elsevier, vol. 142(C), pages 66-79.
    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. Roberto Bruno & Piero Bevilacqua & Antonino Rollo & Francesco Barreca & Natale Arcuri, 2022. "A Novel Bio-Architectural Temporary Housing Designed for the Mediterranean Area: Theoretical and Experimental Analysis," Energies, MDPI, vol. 15(9), pages 1-25, April.
    2. Bruno, Roberto & Bevilacqua, Piero, 2022. "Heat and mass transfer for the U-value assessment of opaque walls in the Mediterranean climate: Energy implications," Energy, Elsevier, vol. 261(PA).
    3. Ghosh, Aritra & Norton, Brian & Duffy, Aidan, 2016. "Behaviour of a SPD switchable glazing in an outdoor test cell with heat removal under varying weather conditions," Applied Energy, Elsevier, vol. 180(C), pages 695-706.
    4. Zhang, Xiang & Rasmussen, Christoffer & Saelens, Dirk & Roels, Staf, 2022. "Time-dependent solar aperture estimation of a building: Comparing grey-box and white-box approaches," Renewable and Sustainable Energy Reviews, Elsevier, vol. 161(C).
    5. Rasooli, Arash & Itard, Laure, 2019. "In-situ rapid determination of walls’ thermal conductivity, volumetric heat capacity, and thermal resistance, using response factors," Applied Energy, Elsevier, vol. 253(C), pages 1-1.

    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. Pisello, Anna Laura & Asdrubali, Francesco, 2014. "Human-based energy retrofits in residential buildings: A cost-effective alternative to traditional physical strategies," Applied Energy, Elsevier, vol. 133(C), pages 224-235.
    2. Buonomano, Annamaria & Montanaro, Umberto & Palombo, Adolfo & Santini, Stefania, 2016. "Dynamic building energy performance analysis: A new adaptive control strategy for stringent thermohygrometric indoor air requirements," Applied Energy, Elsevier, vol. 163(C), pages 361-386.
    3. Vallianos, Charalampos & Candanedo, José & Athienitis, Andreas, 2023. "Application of a large smart thermostat dataset for model calibration and Model Predictive Control implementation in the residential sector," Energy, Elsevier, vol. 278(PA).
    4. Anna Kipping & Erik Trømborg, 2017. "Modeling Aggregate Hourly Energy Consumption in a Regional Building Stock," Energies, MDPI, vol. 11(1), pages 1-20, December.
    5. 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.
    6. Yildiz, B. & Bilbao, J.I. & Sproul, A.B., 2017. "A review and analysis of regression and machine learning models on commercial building electricity load forecasting," Renewable and Sustainable Energy Reviews, Elsevier, vol. 73(C), pages 1104-1122.
    7. 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.
    8. Gokhale, Gargya & Claessens, Bert & Develder, Chris, 2022. "Physics informed neural networks for control oriented thermal modeling of buildings," Applied Energy, Elsevier, vol. 314(C).
    9. Tomasz Szul & Krzysztof Nęcka & Stanisław Lis, 2021. "Application of the Takagi-Sugeno Fuzzy Modeling to Forecast Energy Efficiency in Real Buildings Undergoing Thermal Improvement," Energies, MDPI, vol. 14(7), pages 1-16, March.
    10. Salah Bouktif & Ali Fiaz & Ali Ouni & Mohamed Adel Serhani, 2018. "Optimal Deep Learning LSTM Model for Electric Load Forecasting using Feature Selection and Genetic Algorithm: Comparison with Machine Learning Approaches †," Energies, MDPI, vol. 11(7), pages 1-20, June.
    11. Nweye, Kingsley & Nagy, Zoltan, 2022. "MARTINI: Smart meter driven estimation of HVAC schedules and energy savings based on Wi-Fi sensing and clustering," Applied Energy, Elsevier, vol. 316(C).
    12. Papineau, Maya & Yassin, Kareman & Newsham, Guy & Brice, Sarah, 2021. "Conditional demand analysis as a tool to evaluate energy policy options on the path to grid decarbonization," Renewable and Sustainable Energy Reviews, Elsevier, vol. 149(C).
    13. Wang, Ran & Lu, Shilei & Feng, Wei, 2020. "A three-stage optimization methodology for envelope design of passive house considering energy demand, thermal comfort and cost," Energy, Elsevier, vol. 192(C).
    14. Costa, Andrea & Keane, Marcus M. & Torrens, J. Ignacio & Corry, Edward, 2013. "Building operation and energy performance: Monitoring, analysis and optimisation toolkit," Applied Energy, Elsevier, vol. 101(C), pages 310-316.
    15. Chou, Jui-Sheng & Tran, Duc-Son, 2018. "Forecasting energy consumption time series using machine learning techniques based on usage patterns of residential householders," Energy, Elsevier, vol. 165(PB), pages 709-726.
    16. Gautham Krishnadas & Aristides Kiprakis, 2020. "A Machine Learning Pipeline for Demand Response Capacity Scheduling," Energies, MDPI, vol. 13(7), pages 1-25, April.
    17. Gianluca Serale & Massimo Fiorentini & Alfonso Capozzoli & Daniele Bernardini & Alberto Bemporad, 2018. "Model Predictive Control (MPC) for Enhancing Building and HVAC System Energy Efficiency: Problem Formulation, Applications and Opportunities," Energies, MDPI, vol. 11(3), pages 1-35, March.
    18. Kontoleon, K.J., 2015. "Glazing solar heat gain analysis and optimization at varying orientations and placements in aspect of distributed radiation at the interior surfaces," Applied Energy, Elsevier, vol. 144(C), pages 152-164.
    19. Sun, Xiaoqin & Medina, Mario A. & Lee, Kyoung Ok & Jin, Xing, 2018. "Laboratory assessment of residential building walls containing pipe-encapsulated phase change materials for thermal management," Energy, Elsevier, vol. 163(C), pages 383-391.
    20. Shamsi, Mohammad Haris & Ali, Usman & Mangina, Eleni & O’Donnell, James, 2021. "Feature assessment frameworks to evaluate reduced-order grey-box building energy models," Applied Energy, Elsevier, vol. 298(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:appene:v:160:y:2015:i:c:p:431-441. 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.