## 2 Related Work  
Visual comfort is an important aspect of design and human well-being, particularly in environments where people spend extended periods of time. The term \"visual comfort\" refers to the quality of lighting and visual conditions that enable people to perform tasks without strain or discomfort. A sufficient amount of natural and artificial light, good glare control, and access to views of the outdoors are all characteristics of visual comfort. Due to the numerous parameters that must be considered, the visual comfort of interiors is a complicated topic. In the past few years, a limited number of articles on visual comfort have been published [16]. A summary of various methodologies developed for visual comfort is presented in Table 1. Additionally, a comparison is drawn between the methods used to estimate and predict visual comfort using mathematical equations (non-artificial-intelligence solutions) and machine learning methods (artificial intelligence solutions), as well as the number of parameters, the type of data used, and the results (prediction or real-time calculation). A statistical approach for the evaluation of thermal and visual comfort in free-running buildings was presented in [17]. The approach relied on the use of straightforward indicators to take into account the duration and intensity of potential discomfort. The statistical indicators could be used separately or in combination to obtain useful information regarding the simultaneous effects of a building solution on the thermal and visual comfort. The main drawback of this method was that the authors' model assumed a uniform distri \begin{table}
\begin{tabular}{l c c c c} \hline \hline
**Type of Results** & **Variables** & **Type of Method** & **Accuracy** & **Reference** \\ \hline Real-time & \multirow{2}{*}{Illuminance} & \multirow{2}{*}{non-AI} & \multirow{2}{*}{N/A} & [17] \\ estimation & & & & \\ \hline Prediction & \begin{tabular}{c} Daylight, glare, and \\ view quality metrics \\ \end{tabular} & AI & 97\% & [18] \\ \hline Real-time & \begin{tabular}{c} Daylight factor, \\ glare rating index \\ \end{tabular} & non-AI & 73\% & [19] \\ \hline Prediction & \begin{tabular}{c} Daylight autonomy, \\ glare, and building \\ information \\ \end{tabular} & AI & \(R^{2}=0.941\) & [20] \\ \hline Prediction & \
\begin{tabular}{c} Illuminance, luminance, \\ and glare \\ \end{tabular} & AI & 79.5\% & [21] \\ \hline Real-time & \multirow{2}{*}{
\begin{tabular}{c} Illuminance \\ estimation \\ \end{tabular} } & AI & 99\% & This work \\ \hline \hline \end{tabular}
\end{table} 
Table 1: Comparison of state-of-the-art methods. bution of light levels throughout the space, which may not accurately capture the dynamic and spatially varying nature of visual comfort conditions. In [19], a method for predicting and optimizing the long-term visual comfort of occupants in daylight spaces, such as offices and classrooms, was proposed. The concept utilized a combination of daylight metrics and visual comfort models to develop a framework that considered not only instantaneous visual comfort but also cumulative visual comfort over time. One potential drawback of the concept was that it required more detailed information about the building design, such as the orientation and location of windows, shading devices, and other features affecting daylighting that were missing from the method. Additionally, the calculation of the glare rating index also requires a combination of objective measurements and subjective evaluations, such as surveys and questionnaires, to fully assess the visual comfort and glare in a space, which can sometimes be difficult to acquire. A method based on ANN was introduced in [18], with a particular emphasis on the prediction of daylight and visual comfort metrics in the early design stages. Daylight and visual comfort were analyzed along with view quality metrics. Different architectures were applied, and the results indicated that all three types of metric could be predicted by this method, with an average accuracy close to 97%.