Special Issue "Design of Architectural Sustainable Lighting"

A special issue of Sustainability (ISSN 2071-1050). This special issue belongs to the section "Sustainable Engineering and Science".

Deadline for manuscript submissions: 31 December 2021.

Special Issue Editor

Prof. Dr. Gon Kim
E-Mail Website
Guest Editor
Faculty of Department of Architectural Engineering, Kyung Hee University, Yongin, Korea
Interests: sustainable lighting; daylighting; light pollution; façade engineering; nightscape lighting; building energy; environment-friendly architecture

Special Issue Information

Dear colleagues,

Considering the myriad of issues associated with the production of electricity, such as those related to global warming, the proper design and usage of lighting in buildings has become increasingly important. In addition to the environmental issues, we now know that there are potential linkages between lighting and the psychological as well as physiological well-being of building occupants; it is all a matter of the content of light and how it is engineered. For example, on the one hand, light rich in blue spectral content has been reported to desynchronize the human circadian rhythms during the night hours. On the other hand, properly designed blue light can be used to elicit activeness, thus increasing productivity and work performance in buildings that lack sufficient amounts of daylight during the day.

There is also the issue of light pollution; properly designed lighting can substantially contribute to the prosperity and aesthetics of a built environment. At the same time, poorly designed lighting leads to light pollution, which is a serious problem with its own particular set of adverse effects that include some of the health issues discussed above and which have subsequently dominated the debate in the lighting community in recent years.

However, it has not been all bad news regarding lighting in the built environment; there have been exciting advancements in the technical design of light sources in terms of efficiency and healthy lighting. Additionally, numerous ways of incorporating daylight into built environments to lessen dependency on artificial lighting are continuously being discussed in the lighting community. Moreover, the development and application of new technologies, for example those associated with Internet of Things (IoT) and machine learning, in the field of architectural lighting provide infinite possibilities that could facilitate the design of good, healthy, and economic lighting (e.g., in lighting control mechanisms).

As such, given the importance of lighting in our everyday lives, the extent and the multifaceted nature of light-related issues in the built environment, consistent and multidisciplinary discussions are rather necessary among lighting designers, policy makers, health industry, computer scientists, electrical engineers, and so forth. To that end, this Special Issue, “Design of Architectural Sustainable Lighting”, provides a platform for exchange of knowledge and current status of the design, research, development, engineering experiences, and law regarding sustainable light and lighting issues, covering visual, environmental, social, and economic dimensions and requires a multidisciplinary approach in order to advance in these and related areas.  

Papers selected for this Special Issue will be subject to a rigorous peer review procedure with the aim of rapid and wide dissemination of research results, developments and applications.

Prof. Dr. Gon Kim
Guest Editor

Manuscript Submission Information

Manuscripts should be submitted online at www.mdpi.com by registering and logging in to this website. Once you are registered, click here to go to the submission form. Manuscripts can be submitted until the deadline. All papers will be peer-reviewed. Accepted papers will be published continuously in the journal (as soon as accepted) and will be listed together on the special issue website. Research articles, review articles as well as short communications are invited. For planned papers, a title and short abstract (about 100 words) can be sent to the Editorial Office for announcement on this website.

Submitted manuscripts should not have been published previously, nor be under consideration for publication elsewhere (except conference proceedings papers). All manuscripts are thoroughly refereed through a single-blind peer-review process. A guide for authors and other relevant information for submission of manuscripts is available on the Instructions for Authors page. Sustainability is an international peer-reviewed open access semimonthly journal published by MDPI.

Please visit the Instructions for Authors page before submitting a manuscript. The Article Processing Charge (APC) for publication in this open access journal is 1900 CHF (Swiss Francs). Submitted papers should be well formatted and use good English. Authors may use MDPI's English editing service prior to publication or during author revisions.

Keywords

  • Sustainable lighting
  • Daylighting in architecture: design and analysis
  • Innovative façade lighting
  • Energy conservative lighting
  • Right to light and policies
  • Light pollution and visual comfort
  • Lighting controls and modeling
  • Lighting issues for CPTED (crime prevention through environmental design),
  • Healthy lighting and light therapy
  • Innovative lighting fixture design and technologies
  • Indoor and outdoor lighting

Published Papers (3 papers)

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Research

Article
Using Simulation-Based Modeling to Evaluate Light Trespass in the Design Stage of Sports Facilities
Sustainability 2021, 13(9), 4725; https://doi.org/10.3390/su13094725 - 23 Apr 2021
Viewed by 433
Abstract
Artificial light is a pollutant with broad implications for society. Consequently, laws and regulations aimed at curbing the improper use of artificial lighting (i.e., light pollution) are becoming common. For such laws to become effective, light pollution must first be evaluated and quantified. [...] Read more.
Artificial light is a pollutant with broad implications for society. Consequently, laws and regulations aimed at curbing the improper use of artificial lighting (i.e., light pollution) are becoming common. For such laws to become effective, light pollution must first be evaluated and quantified. Current methods for evaluating lit environments in sports facilities are only applicable after the facilities have been built, making it challenging to prevent light pollution preemptively. Here, a technique to predict light pollution during the planning stage is proposed. To analyze the upward light generated by the large lights found in sports facilities using a computer simulation, a method of defining a virtual horizontal board above the stadium is proposed. To analyze how light trespasses out of the stadium, a method of dividing and analyzing the space outside the stadium by zone is also presented. In addition, a method to predict the formation of high luminance surfaces under outdoor lighting is presented. The proposed methods are validated on two large sports facilities currently being constructed in Korea. As a consequence, horizontal and vertical illuminances are reduced by 74.5% and 72.2%, respectively. The methods are useful to lighting designers with interest in reducing light pollution. Full article
(This article belongs to the Special Issue Design of Architectural Sustainable Lighting)
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Article
Optimization of Daylighting Pattern of Museum Sculpture Exhibition Hall
Sustainability 2021, 13(4), 1918; https://doi.org/10.3390/su13041918 - 10 Feb 2021
Cited by 2 | Viewed by 654
Abstract
In this study, based on the current daylighting situation of a museum sculpture exhibition hall, the exhibition space is classified according to the daylighting requirements of the sculptures. Additionally, the daylighting pattern for the sculpture exhibition hall and the display layout of the [...] Read more.
In this study, based on the current daylighting situation of a museum sculpture exhibition hall, the exhibition space is classified according to the daylighting requirements of the sculptures. Additionally, the daylighting pattern for the sculpture exhibition hall and the display layout of the exhibits are summarized. The daylighting parameters of the exhibition space under different scenarios are calculated. The exhibition space is simulated and analyzed under three daylighting conditions (flat skylights, flat skylights with side windows, and flat skylights with high side windows), and the daylighting parameters are optimized based on the daylighting patterns and components. It is discovered that with the combination of flat skylights and high side windows, the daylight factor (DF) and uniformity of daylighting (UD) of the sculpture exhibition as well as glare rating of the windows are the most favorable. Therefore, the appropriate daylighting pattern and components are determined, and the corresponding optimization strategy for daylighting is proposed. The results show that the daylighting optimization strategy proposed herein can improve the daylighting quality of the museum sculpture exhibition space and yield a suitable light environment. Full article
(This article belongs to the Special Issue Design of Architectural Sustainable Lighting)
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Article
Comparative Performance of Machine Learning Algorithms in the Prediction of Indoor Daylight Illuminances
Sustainability 2020, 12(11), 4471; https://doi.org/10.3390/su12114471 - 01 Jun 2020
Cited by 8 | Viewed by 881
Abstract
The performance of machine learning (ML) algorithms depends on the nature of the problem at hand. ML-based modeling, therefore, should employ suitable algorithms where optimum results are desired. The purpose of the current study was to explore the potential applications of ML algorithms [...] Read more.
The performance of machine learning (ML) algorithms depends on the nature of the problem at hand. ML-based modeling, therefore, should employ suitable algorithms where optimum results are desired. The purpose of the current study was to explore the potential applications of ML algorithms in modeling daylight in indoor spaces and ultimately identify the optimum algorithm. We thus developed and compared the performance of four common ML algorithms: generalized linear models, deep neural networks, random forest, and gradient boosting models in predicting the distribution of indoor daylight illuminances. We found that deep neural networks, which showed a determination of coefficient (R2) of 0.99, outperformed the other algorithms. Additionally, we explored the use of long short-term memory to forecast the distribution of daylight at a particular future time. Our results show that long short-term memory is accurate and reliable (R2 = 0.92). Our findings provide a basis for discussions on ML algorithms’ use in modeling daylight in indoor spaces, which may ultimately result in efficient tools for estimating daylight performance in the primary stages of building design and daylight control schemes for energy efficiency. Full article
(This article belongs to the Special Issue Design of Architectural Sustainable Lighting)
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