## 6 Conclusions  
In conclusion, this paper presented a groundbreaking method for assessing visual comfort in real-world occupied buildings, addressing a significant research gap in evaluating visual comfort once a building is in use. The proposed non-intrusive and practical approach incorporated mathematical visual comfort estimation into a regression model, yielding exceptional results, with an impressive RMSE of 0.0034. This method holds great potential for real-life and real-time applications, particularly in the context of smart homes. By accurately estimating visual comfort, smart home systems can dynamically adjust lighting conditions, color schemes, and other factors to optimize occupant well-being,productivity, and satisfaction. The remarkable performance improvement of 99% and significant increase in the processing time of 95% further highlight the practicality and efficiency of this method for real-time implementation. The continuous development and refinement of this approach offer immense potential for enhancing the quality of indoor environments and creating personalized and adaptive smart home systems that prioritize visual comfort for occupants. Future work in this area could build upon the success of this method by exploring its applicability to different building types and environments. Additionally, further research could focus on incorporating additional factors and variables that contribute to visual comfort, such as lighting conditions, color schemes, and furniture arrangements. This would enable a more comprehensive and holistic assessment of visual comfort, providing valuable insights for architects, designers, and facility managers to optimize indoor spaces for enhanced occupant experiences. Moreover, ongoing efforts should be directed towards refining the regression model and expanding the dataset to improve the accuracy and generalizability of the visual assessment method. By continuously advancing and refining these techniques, we can continually improve the quality of indoor environments and ensure optimal visual comfort for building occupants. The limitations of this work should be acknowledged to provide a comprehensive understanding of its scope and potential challenges. Firstly, the proposed approach relied on a mathematical visual comfort estimation, which may have introduced certain assumptions and simplifications. Although it outperformed a traditional mathematical model, there is still room for improvement in accurately capturing the complex dynamics of visual comfort in diverse indoor environments. Additionally, the study assumed an ideal installation point for the sensor, which may not always be achievable in practice. Variations in sensor placement could affect the accuracy of the results, and further research is needed to explore strategies for mitigating this limitation. In practical implementation, the results of this study offer valuable insights for enhancing visual comfort in indoor environments. The superior performance and computational efficiency of the proposed approach make it a promising tool for architects, designers, and building professionals to optimize lighting conditions and create visually comfortable spaces. By incorporating the estimated visual comfort values into the design process, practitioners could make informed decisions regarding the selection of lighting fixtures, the placement of windows, and the implementation of shading devices. This could lead to improved occupant well-being, productivity, and satisfaction. To address the potential limitation of sensor installation, future research should focus on developing strategies to account for inaccuracies resulting from sensor placement. This could involve exploring sensor calibration techniques, conducting sensitivity analyses to identify the impact of different installation points, and even considering multiple sensors placed strategically within the indoor environment to capture a more comprehensive view of visual comfort.