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Daylight availability assessment and its potential energy saving estimation –A literature review

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  • Yu, Xu
  • Su, Yuehong

Abstract

Daylight has been recognized as a free energy and cost effective alternative to artificial lighting for sustainable building design. It could also provide a more pleasant and attractive indoor environment for its good color rendering and it matches the human visual response much better than artificial light source. The effects of daylight on the occupancy comfort, health, well-being and productivity are well acknowledged. However, despite the above advantages, daylight is not widely integrated with the artificial lighting in building design as there is lack of information and evaluation tools on the suitability of daylight and its energy saving potential. The procedure of evaluating the indoor daylight performance consists of two parts: one is to assess the indoor daylight availability by either simulation or field measurements; another one is to accurately estimate potential energy saving when sufficient daylight is provided. This paper therefore presents a review of metrics and methods for indoor daylight availability assessment, as well as estimation methods used for predicting potential energy saving from daylight. The aim of this paper is to provide building designers or operators some useful information so that more daylight schemes could be considered in sustainable building design.

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  • 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.
    • Handle: RePEc:eee:rensus:v:52:y:2015:i:c:p:494-503
    DOI: 10.1016/j.rser.2015.07.142
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    References listed on IDEAS

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    8. Jie Li & Qichao Ban & Xueming (Jimmy) Chen & Jiawei Yao, 2019. "Glazing Sizing in Large Atrium Buildings: A Perspective of Balancing Daylight Quantity and Visual Comfort," Energies, MDPI, vol. 12(4), pages 1-14, February.
    9. Ignacio Acosta & Miguel Ángel Campano & Samuel Domínguez-Amarillo & Carmen Muñoz, 2018. "Dynamic Daylight Metrics for Electricity Savings in Offices: Window Size and Climate Smart Lighting Management," Energies, MDPI, vol. 11(11), pages 1-27, November.
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    13. Qiu, Changyu & Yang, Hongxing, 2020. "Daylighting and overall energy performance of a novel semi-transparent photovoltaic vacuum glazing in different climate zones," Applied Energy, Elsevier, vol. 276(C).
    14. Košir, Mitja & Iglič, Nataša & Kunič, Roman, 2018. "Optimisation of heating, cooling and lighting energy performance of modular buildings in respect to location’s climatic specifics," Renewable Energy, Elsevier, vol. 129(PA), pages 527-539.
    15. Qiu, Changyu & Yi, Yun Kyu & Wang, Meng & Yang, Hongxing, 2020. "Coupling an artificial neuron network daylighting model and building energy simulation for vacuum photovoltaic glazing," Applied Energy, Elsevier, vol. 263(C).
    16. Bonomolo, Marina & Zizzo, Gaetano & Ferrari, Simone & Beccali, Marco & Guarino, Stefania, 2021. "Empirical BAC factors method application to two real case studies in South Italy," Energy, Elsevier, vol. 236(C).
    17. Simplice Igor Noubissie Tientcheu & Shyama P. Chowdhury & Thomas O. Olwal, 2019. "Intelligent Energy Management Strategy for Automated Office Buildings," Energies, MDPI, vol. 12(22), pages 1-27, November.
    18. Sara Eriksson & Lovisa Waldenström & Max Tillberg & Magnus Österbring & Angela Sasic Kalagasidis, 2019. "Numerical Simulations and Empirical Data for the Evaluation of Daylight Factors in Existing Buildings in Sweden," Energies, MDPI, vol. 12(11), pages 1-24, June.
    19. Nataša Šprah & Mitja Košir, 2019. "Daylight Provision Requirements According to EN 17037 as a Restriction for Sustainable Urban Planning of Residential Developments," Sustainability, MDPI, vol. 12(1), pages 1-22, December.
    20. Jack Ngarambe & Amina Irakoze & Geun Young Yun & Gon Kim, 2020. "Comparative Performance of Machine Learning Algorithms in the Prediction of Indoor Daylight Illuminances," Sustainability, MDPI, vol. 12(11), pages 1-22, June.
    21. Yue Ying & Mila Koeva & Monika Kuffer & Kwabena Obeng Asiama & Xia Li & Jaap Zevenbergen, 2020. "Making the Third Dimension (3D) Explicit in Hedonic Price Modelling: A Case Study of Xi’an, China," Land, MDPI, vol. 10(1), pages 1-26, December.
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