A smart scoring method for the assessment of office lighting systems. (1st December 2022)

• Record Type: Journal Article
• Authors: Chen, Po-Han; Huang, Ting-Ya; Chang, Woei-Chyi; Lin, Yi-Hsin
• Is Part Of: Journal of building engineering. Volume 61(2022)
• Publication Date: 2022-12-01
• Subjects: Office lighting -- Deep neural networks -- Lighting quality index -- DIALux; Building -- Periodicals; 690.05
• DOI: 10.1016/j.jobe.2022.105258
• Languages: English
• ISSNs: 2352-7102

Abstract

Abstract: Excessive energy consumption is a crucial global issue nowadays and lighting contributes a lot to daily energy consumption, especially that in the office. Hence, an efficient and effective method for evaluating office lighting design is necessary for saving energy in office lighting. Presently, there are two common issues associated with lighting design. First, the lighting indices are useful, but somewhat hard to comprehend. Second, a large number of lights and lighting designs are available, which sometimes makes it hard for decision-making. Therefore, this study established a scoring method for designers to efficiently understand the meaning of four lighting indices-average illuminance, unified glare rating, uniformity, and unit lighting power density-based on expert experience. Meanwhile, to tackle the decision-making issue, deep learning technique was applied to provide the suggestion of the optimal lighting design, including light type, the arrangement of lights, cost, and the score of four lighting indices. The results showed that the suggested design can obtain 93 points based on the proposed scoring method. The contributions of this study lie in (1) facilitating a straightforward understanding of four light indices and (2) providing a suggestion of lighting design for designers. Highlights: A scoring method for lighting four indices was developed based on expert experience. A developed deep neural network can come up with cost- and energy-effective lighting designs. Best lighting design solution can be generated based on "lighting quality" or "configuration costs.".