Illumination; its Distribution and Measurement

Trotter, A.P.

doi:10.1038/088072a0

Cited by 9 publications

(2 citation statements)

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“…In the past, many authors have made important contributions on this topic. The most important parameter used in building design is the daylight factor (DF), defined by Trotter [21]. The estimation of this was improved by Dresler [22] by formulating empirical relationships.…”

Section: Literature Reviewmentioning

confidence: 99%

Evaluation of the electricity savings resulting from a control system for artificial lights based on the available daylight

Nicoletti

Ferraro²,

Kaliakatsos³

et al. 2022

Acta IMEKO

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Natural lighting in building environments is an important aspect for the occupants' mental and physical health. Furthermore, the proper exploitation of this resource can bring energy benefits related to the reduced use of artificial lighting. This work provides some estimates of the energy that can be saved by using a lighting system that recognises indoor illuminance. In particular, it is able to manage the switching on of lights according to the daylight detected in the room. The savings from this solution depend on the size and orientation of the window. The analysis is conducted on an office by means of simulations using the INLUX-DBR code. The locations have an influence on the luminance characteristics of the sky. The analysis is conducted with reference to one city in the south and one in the north of Italy (Cosenza and Milan). The energy saving is almost independent of latitude and therefore representative of the Italian territory. it is highly variable according to exposure, being the highest for southern exposure (97% with the window size equal to 36% of the floor area) and between 26% and 48% (as a function of window size) for northern exposure.

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Section: Literature Reviewmentioning

confidence: 99%

Evaluation of the electricity savings resulting from a control system for artificial lights based on the available daylight

Nicoletti

Ferraro²,

Kaliakatsos³

et al. 2022

Acta IMEKO

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“…These methods are static, dynamic, climate-based, and machinelearning models. The static method applied Daylight Factor (DF), which was introduced by Trotter (1911) to assess daylighting. The components of DF can be calculated using graphical methods.…”

Section: Introductionmentioning

confidence: 99%

Useful Daylight Illuminance Prediction Under Data Imbalance in an Urban Context

Canli,

Gursel Dino,

Kalkan

2023

eCAADe Proceedings

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Optimal daylight illumination can aid sustainable design by improving occupants' psychological and physical health, visual and thermal comfort and decreasing electrical lighting energy usage in buildings. However, dense urban areas can result in restricted daylight access in buildings. Therefore, daylight analysis considering surrounding buildings is important for implementing daylighting strategies. Useful Daylight Illuminance (UDI) is a performance metric that can quantify the annual illuminance levels within certain illumination classes (UDIfell-short, UDIsupplementary, UDIautonomous, and UDIexceeded). UDI can be predicted using machine-learning (ML) methods. However, the calculated data is typically unevenly distributed, generally following a power-law distribution, which causes ML models to underperform for UDI classes with less data. Simulations can be utilized to increase the less dispersed data in the dataset; however, at the urban scale, the computational cost of collecting simulation data for daylighting analysis makes it difficult to augment data with simulations. To undertake this challenge, in this study, SMOTE (Synthetic Minority Oversampling Technique) was applied to augment data to increase the prediction performance of the ML model. The results showed that augmenting the data in the classes which are unevenly distributed leads to an increase in ML model prediction performance. This method shows that SMOTE can be used to increase the performance of ML models during UDI estimation at the urban scale.

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