IDEAS home Printed from https://ideas.repec.org/a/gam/jeners/v18y2025i15p4113-d1716385.html
   My bibliography  Save this article

Integration of Daylight in Building Design as a Way to Improve the Energy Efficiency of Buildings

Author

Listed:
  • Adrian Trząski

    (Division of Air Conditioning and Heating, Faculty of Environmental Engineering, Warsaw University of Technology, Nowowiejska 20, 00-653 Warsaw, Poland)

  • Joanna Rucińska

    (Division of Air Conditioning and Heating, Faculty of Environmental Engineering, Warsaw University of Technology, Nowowiejska 20, 00-653 Warsaw, Poland)

Abstract

According to the United Nations Environment Programme reports, buildings are responsible for nearly 40% of energy-related emissions; therefore, energy-optimized building design is crucial to reduce the reliance on non-renewable energy sources as well as greenhouse gas emissions. The OECD reports indicate the use of Building Information Modelling (BIM) as one of the effective strategies for decarbonization of buildings, since a 3D digital representation of both physical and functional characteristics of a building can help to design a more efficient infrastructure. An efficient integration of solar energy in building design can be vital for the enhancement of energy performance in terms of heating, cooling, and lighting demand. This paper presents results of an analysis of how factors related to the use of daylight, such as automatic control of artificial lighting, external shading, or the visual absorptance of internal surfaces, influence the energy efficiency within an example room in two different climatic zones. The simulation was conducted using Design Builder software, with predefined occupancy schedules and internal heat gains, and standard EPW weather files for Warsaw and Genua climate zones. The study indicates that for the examined room, when no automatic sunshades or a lighting control system is utilized, most of the final energy demand is for cooling purposes (45–54%), followed by lighting (42–43%), with only 3–12% for heating purposes. The introduction of sunshades and/or the use of daylight allowed for a reduction of the total demand by up to half. Moreover, it was pointed out that often neglected factors, like the colour of the internal surfaces, can have a significant effect on the final energy consumption. In variants with light interior, the total energy consumption was lower by about 3–4% of the baseline demand, compared to their corresponding ones with dark surfaces. These results are consistent with previous studies on daylighting strategies and highlight the importance of considering both visual and thermal impacts when evaluating energy performance. Similarly, possible side effects of certain actions were highlighted, such as an increase in heat demand resulting from a reduced need for artificial lighting. The results of the analysis highlight the potential of a simulation-based design approach in optimizing daylight use, contributing to the broader goals of building decarbonization.

Suggested Citation

  • Adrian Trząski & Joanna Rucińska, 2025. "Integration of Daylight in Building Design as a Way to Improve the Energy Efficiency of Buildings," Energies, MDPI, vol. 18(15), pages 1-14, August.
  • Handle: RePEc:gam:jeners:v:18:y:2025:i:15:p:4113-:d:1716385
    as

    Download full text from publisher

    File URL: https://www.mdpi.com/1996-1073/18/15/4113/pdf
    Download Restriction: no

    File URL: https://www.mdpi.com/1996-1073/18/15/4113/
    Download Restriction: no
    ---><---

    References listed on IDEAS

    as
    1. June Hae Lee & Jae-Sik Kang, 2024. "Study on Lighting Energy Savings by Applying a Daylight-Concentrating Indoor Louver System with LED Dimming Control," Energies, MDPI, vol. 17(14), pages 1-11, July.
    2. Eleftheria Touloupaki & Theodoros Theodosiou, 2017. "Performance Simulation Integrated in Parametric 3D Modeling as a Method for Early Stage Design Optimization—A Review," Energies, MDPI, vol. 10(5), pages 1-18, May.
    3. Jakob Carlander & Bahram Moshfegh & Jan Akander & Fredrik Karlsson, 2020. "Effects on Energy Demand in an Office Building Considering Location, Orientation, Façade Design and Internal Heat Gains—A Parametric Study," Energies, MDPI, vol. 13(23), pages 1-22, November.
    4. Li, Danny H.W. & Lam, Tony N.T. & Wong, S.L. & Tsang, Ernest K.W., 2008. "Lighting and cooling energy consumption in an open-plan office using solar film coating," Energy, Elsevier, vol. 33(8), pages 1288-1297.
    5. Nguyen, Anh-Tuan & Reiter, Sigrid & Rigo, Philippe, 2014. "A review on simulation-based optimization methods applied to building performance analysis," Applied Energy, Elsevier, vol. 113(C), pages 1043-1058.
    6. Pisello, Anna Laura & Goretti, Michele & Cotana, Franco, 2012. "A method for assessing buildings’ energy efficiency by dynamic simulation and experimental activity," Applied Energy, Elsevier, vol. 97(C), pages 419-429.
    7. Lee, J.W. & Jung, H.J. & Park, J.Y. & Lee, J.B. & Yoon, Y., 2013. "Optimization of building window system in Asian regions by analyzing solar heat gain and daylighting elements," Renewable Energy, Elsevier, vol. 50(C), pages 522-531.
    Full references (including those not matched with items on IDEAS)

    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. Nayara R. M. Sakiyama & Joyce C. Carlo & Leonardo Mazzaferro & Harald Garrecht, 2021. "Building Optimization through a Parametric Design Platform: Using Sensitivity Analysis to Improve a Radial-Based Algorithm Performance," Sustainability, MDPI, vol. 13(10), pages 1-25, May.
    2. Lešnik, Maja & Kravanja, Stojan & Premrov, Miroslav & Žegarac Leskovar, Vesna, 2020. "Optimal design of timber-glass upgrade modules for vertical building extension from the viewpoints of energy efficiency and visual comfort," Applied Energy, Elsevier, vol. 270(C).
    3. Prieto, Alejandro & Knaack, Ulrich & Klein, Tillmann & Auer, Thomas, 2017. "25 Years of cooling research in office buildings: Review for the integration of cooling strategies into the building façade (1990–2014)," Renewable and Sustainable Energy Reviews, Elsevier, vol. 71(C), pages 89-102.
    4. Karmellos, M. & Kiprakis, A. & Mavrotas, G., 2015. "A multi-objective approach for optimal prioritization of energy efficiency measures in buildings: Model, software and case studies," Applied Energy, Elsevier, vol. 139(C), pages 131-150.
    5. Delgarm, N. & Sajadi, B. & Kowsary, F. & Delgarm, S., 2016. "Multi-objective optimization of the building energy performance: A simulation-based approach by means of particle swarm optimization (PSO)," Applied Energy, Elsevier, vol. 170(C), pages 293-303.
    6. Abdo Abdullah Ahmed Gassar & Choongwan Koo & Tae Wan Kim & Seung Hyun Cha, 2021. "Performance Optimization Studies on Heating, Cooling and Lighting Energy Systems of Buildings during the Design Stage: A Review," Sustainability, MDPI, vol. 13(17), pages 1-47, September.
    7. Balvís, Eduardo & Sampedro, Óscar & Zaragoza, Sonia & Paredes, Angel & Michinel, Humberto, 2016. "A simple model for automatic analysis and diagnosis of environmental thermal comfort in energy efficient buildings," Applied Energy, Elsevier, vol. 177(C), pages 60-70.
    8. Kim, Wonuk & Jeon, Yongseok & Kim, Yongchan, 2016. "Simulation-based optimization of an integrated daylighting and HVAC system using the design of experiments method," Applied Energy, Elsevier, vol. 162(C), pages 666-674.
    9. Yu, Xu & Su, Yuehong, 2015. "Daylight availability assessment and its potential energy saving estimation –A literature review," Renewable and Sustainable Energy Reviews, Elsevier, vol. 52(C), pages 494-503.
    10. Ascione, Fabrizio & Bianco, Nicola & De Stasio, Claudio & Mauro, Gerardo Maria & Vanoli, Giuseppe Peter, 2016. "Multi-stage and multi-objective optimization for energy retrofitting a developed hospital reference building: A new approach to assess cost-optimality," Applied Energy, Elsevier, vol. 174(C), pages 37-68.
    11. Yang, Zheng & Becerik-Gerber, Burcin, 2015. "A model calibration framework for simultaneous multi-level building energy simulation," Applied Energy, Elsevier, vol. 149(C), pages 415-431.
    12. Kahsay, Meseret T. & Bitsuamlak, Girma T. & Tariku, Fitsum, 2021. "Thermal zoning and window optimization framework for high-rise buildings," Applied Energy, Elsevier, vol. 292(C).
    13. Simone Giostra & Ayush Kamalia & Gabriele Masera, 2024. "Solar Species: Energy Optimization of Urban Form Through an Evolutionary Design Process," Sustainability, MDPI, vol. 16(21), pages 1-27, October.
    14. Benedek Kiss & Jose Dinis Silvestre & Rita Andrade Santos & Zsuzsa Szalay, 2021. "Environmental and Economic Optimisation of Buildings in Portugal and Hungary," Sustainability, MDPI, vol. 13(24), pages 1-19, December.
    15. Kylili, Angeliki & Fokaides, Paris A. & Christou, Petros & Kalogirou, Soteris A., 2014. "Infrared thermography (IRT) applications for building diagnostics: A review," Applied Energy, Elsevier, vol. 134(C), pages 531-549.
    16. Guariso, Giorgio & Sangiorgio, Matteo, 2019. "Multi-objective planning of building stock renovation," Energy Policy, Elsevier, vol. 130(C), pages 101-110.
    17. Bayoumi, Mohannad & Fink, Dietrich, 2014. "Maximizing the performance of an energy generating façade in terms of energy saving strategies," Renewable Energy, Elsevier, vol. 64(C), pages 294-305.
    18. Sun, Alexander Y., 2020. "Optimal carbon storage reservoir management through deep reinforcement learning," Applied Energy, Elsevier, vol. 278(C).
    19. Giacomo Chiesa & Andrea Acquaviva & Mario Grosso & Lorenzo Bottaccioli & Maurizio Floridia & Edoardo Pristeri & Edoardo Maria Sanna, 2019. "Parametric Optimization of Window-to-Wall Ratio for Passive Buildings Adopting A Scripting Methodology to Dynamic-Energy Simulation," Sustainability, MDPI, vol. 11(11), pages 1-30, May.
    20. Chenhao Zhu & Jonah Susskind & Mario Giampieri & Hazel Backus O’Neil & Alan M. Berger, 2023. "Optimizing Sustainable Suburban Expansion with Autonomous Mobility through a Parametric Design Framework," Land, MDPI, vol. 12(9), pages 1-31, September.

    More about this item

    Keywords

    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;
    ;

    Statistics

    Access and download statistics

    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:gam:jeners:v:18:y:2025:i:15:p:4113-:d:1716385. 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: MDPI Indexing Manager (email available below). General contact details of provider: https://www.mdpi.com .

    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.