## 1 Introduction
All in all, monitoring visual comfort in indoor spaces can be challenging, because it depends on subjective evaluations from occupants and can vary based on individual preferences and needs [13]. However, there are some strategies that can be used to monitor and improve visual comfort in indoor spaces, such as conducting regular occupant surveys, implementing flexible lighting controls, using advanced lighting technologies, and working with lighting professionals [14]. While monitoring visual comfort in indoor spaces can be difficult, these strategies can help ensure that occupants are comfortable and productive in their work environments. However, the main problem of estimating practical visual comfort in indoor environments remains. The evaluation of visual comfort in occupied buildings faces a significant challenge due to its subjective nature, the limitations of traditional methods relying on occupant surveys, and the lack of methods allowing accurate real-time visual comfort estimation [15]. The proposed approach presents a novel solution by leveraging machine learning techniques and non-intrusive illuminance measurements to estimate visual comfort. This approach aimed to address the existing research gap by offering a practical, objective, and standardized evaluation method. Utilizing machine learning algorithms, it provides a more accurate and consistent assessment of visual comfort, while the use of easily measurable illuminance values ensures accessibility and applicability in diverse indoor environments. With its focus on practicality, non-invasiveness, and precise outcomes, the proposed approach holds substantial significance in filling this research gap and improving the evaluation of visual comfort in real-world and real-time settings and applications. As a result, the aim of this work was to address the challenge of estimating visual comfort in real time in a non-intrusive manner using feasible and easily measurable values. The proposed method leverages machine learning (ML) techniques and relies solely on illuminance values obtained from sensors, making it practical and non-invasive. By developing a regression model that incorporates a mathematical estimation of visual comfort, this research sought to provide precise outcomes for assessing light quality while utilizing minimal available information. Evaluating the proposed approach through real-world use cases allowed for a comparison of the results, validating the method's effectiveness. This work is of the utmost importance, as it aligns with the broader goals of smart home integration, aiming to enhance energy efficiency and indoor environmental quality. By accurately estimating visual comfort using non-intrusive methods, the proposed approach contributes to creating more comfortable and sustainable living environments within smart homes. The structure of the rest of the paper is as follows: Section 2 presents related methods for estimating indoor visual comfort. In Section 3, the methodology followed to assess visual comfort is presented. Section 4 describes the experimental setup along with the results of the proposed methodology. The discussion section compares the proposed approach with those of other papers and elaborates on the practical implications and potential applications of the proposed method. Finally, conclusions are drawn in Section 6.