In interior design, lighting is crucial because it affects a room's mood, practicality, and beauty. This article delves into the complexities of lighting and its effects on interior spaces, looking at how various lighting methods and technologies can complement or detract from the design. The study highlights the particular contributions of ambient, task, accent, and decorative lighting to creating visually and emotionally engaging spaces by evaluating these types of lighting.

The study explores lighting design principles, such as the significance of color temperature, distribution, intensity, and light quality. It demonstrates how well-planned lighting can change how people see a room, draw attention to specific architectural details, and serve the room's function. Natural light and sustainable lighting options, including intelligent lighting systems and LED technology, are also considered potential avenues for enhancing energy efficiency and minimizing environmental impact.

The paper shows how lighting may improve spatial experiences and fulfill various functional needs through case studies of homes, businesses, and public areas. The results show that good lighting is more than just a technical factor; it's a crucial part of interior design that significantly impacts people's health and happiness.

The article finishes with some recommendations for interior designers on illuminating a space in a way that is both aesthetically pleasing and environmentally friendly. It promotes the idea that designers, architects, and lighting experts should work together in an ever-evolving fashion to make interior spaces that are aesthetically pleasing and functionally sound for their inhabitants.

Practicality, color temperature, distribution

Kim, T., Choi, K., & Suk, H. J. (2022). Affective responses to chromatic ambient light in a vehicle. [PDF]

Nakayama, T., Matsushita, Y., Sakayori, K., Sato, R., & Kobayashi, N. (2023). Borrowable Fractional Ownership Types for Verification. [PDF]

Walch, A., Schwärzler, M., Luksch, C., Eisemann, E., & Gschwandtner, T. (2019). LightGuider: Guiding Interactive Lighting Design using Suggestions, Provenance, and Quality Visualization. [PDF]

Xu, Y. & Yu, T. T. (2022). Visual Performance of Psychological Factors in Interior Design Under the Background of Artificial Intelligence. ncbi.nlm.nih.gov

Ru, T., Ma, Y., Zhong, L., Chen, Q., Ma, Y., & Zhou, G. (2022). Effects of Ambient Illuminance on Explicit and Implicit Altruism: The Mediation Roles of Perceived Anonymity and Satisfaction with Light. ncbi.nlm.nih.gov

Xie, X., Cai, J., Fang, H., Tang, X., & Yamanaka, T. (2022). Effects of colored lights on an individual's affective impressions in the observation process. ncbi.nlm.nih.gov

Kyoung Kim, M. (2023). Analysing user sentiment data for architectural interior spaces. [PDF]

Xylakis, E., Liapis, A., & N. Yannakakis, G. (2021). Architectural Form and Affect: A Spatiotemporal Study of Arousal. [PDF]

Lin, J., Olof Hedekvist, P., Mylly, N., Bollen, M., Shen, J., Xiong, J., & Silfvenius, C. (2024). Human-Centric and Integrative Lighting Asset Management in Public Libraries: Qualitative Insights and Challenges from a Swedish Field Study. [PDF]

Pelowski, M., Graser, A., Specker, E., Forster, M., von Hinüber, J., & Leder, H. (2019). Does Gallery Lighting Really Have an Impact on Appreciation of Art? An Ecologically Valid Study of Lighting Changes and the Assessment and Emotional Experience With Representational and Abstract Paintings. ncbi.nlm.nih.gov

E. Crews, D. (2022). Aging, frailty, and design of built environments. ncbi.nlm.nih.gov

Lipp, L., Hahn, D., Ecormier-Nocca, P., Rist, F., & Wimmer, M. (2023). View-Independent Adjoint Light Tracing for Lighting Design Optimization. [PDF]

Kubba, S. (2015). Indoor Environmental Quality (IEQ). ncbi.nlm.nih.gov

Manuel Fernández-Ahumada, L., Osuna-Mérida, M., López-Sánchez, J., Javier Gómez-Uceda, F., López-Luque, R., & Varo-Martínez, M. (2022). Use of Polar Heliostats to Improve Levels of Natural Lighting inside Buildings with Little Access to Sunlight. ncbi.nlm.nih.gov

Xu, Y. & Wu, S. (2022). Indoor Color and Space Humanized Design Based on Emotional Needs. ncbi.nlm.nih.gov

Drugarin, A. & Victoria, C. (2014). A Software Design through Electrical System for a Building. [PDF]

Kim, Y. & Yang, E. (2022). Academic library spaces and student activities during the COVID-19 pandemic. ncbi.nlm.nih.gov

Bellia, L. & Fragliasso, F. (2021). Good Places to Live and Sleep Well: A Literature Review about the Role of Architecture in Determining Non-Visual Effects of Light. ncbi.nlm.nih.gov

Tsesmelis, T., Hasan, I., Cristani, M., Del Bue, A., & Galasso, F. (2019). Human-centric light sensing and estimation from RGBD images: The invisible light switch. [PDF]

Kubba, S. (2016). Indoor Environmental Quality. ncbi.nlm.nih.gov

Kubba, S. (2010). Indoor Environmental Quality. ncbi.nlm.nih.gov

Soler, R. & Voss, E. (2021). Biologically Relevant Lighting: An Industry Perspective. ncbi.nlm.nih.gov

Jiang, N. (2022). Smart Home Product Layout Design Method Based on Real-Number Coding Genetic Algorithm. ncbi.nlm.nih.gov

Arakistain, I. & Barrado, M. (2013). WoodTouch, a new interaction interface for wooden furniture. [PDF]

Borgestrand Øien, T. (2022). A Study of Environmental Factors in Low Vision Rehabilitation. ncbi.nlm.nih.gov

Shahidi, R., Golmohammadi, R., Babamiri, M., Faradmal, J., & Aliabadi, M. (2021). Effect of warm/cool white lights on visual perception and mood in warm/cool color environments. ncbi.nlm.nih.gov

Varshney, A., Soleiman, A., & Voigt, T. (2020). TunnelScatter: Low Power Communication for Sensor Tags using Tunnel Diodes. [PDF]

Wang, Y., Hu, J., Jia, H., Hu, W., Hassan, M., Uddin, A., Kusy, B., & Youssef, M. (2022). Spectral-Loc: Indoor Localization using Light Spectral Information. [PDF]

Mostafavi, A., Bill Xu, T., & Kalantari, S. (2023). Assessing the Effects of Illuminance and Correlated Color Temperature on Emotional Responses and Lighting Preferences Using Virtual Reality. [PDF]

Higuera, J., Llenas, A., & Carreras, J. (2018). Trends in smart lighting for the Internet of Things. [PDF]

D. Elvidge, C., M. Keith, D., T. Tuttle, B., & E. Baugh, K. (2010). Spectral Identification of Lighting Type and Character. ncbi.nlm.nih.gov

Li, T., Shen, H., Yuan, Q., & Zhang, L. (2018). Geographically and temporally weighted neural networks for satellite-based mapping of ground-level PM2.5. [PDF]

Wang, X. & Wang, L. (2022). Intelligent Light Control System Based on Zigbee. ncbi.nlm.nih.gov

Anand, P., Cheong, D., & Sekhar, C. (2021). A review of occupancy-based building energy and IEQ controls and its future post-COVID. ncbi.nlm.nih.gov

Yang, Y., Yang, Y., Chen, M., Feng, C., Xia, H., Cui, S., & Vincent Poor, H. (2021). Joint LED Selection and Precoding Optimization for Multiple-User Multiple-Cell VLC Systems. [PDF]

Chandan, V. (2021). Decentralized Thermal Control of Buildings. [PDF]

Sambandam Raju, P., Mahalingam, M., & Arumugam Rajendran, R. (2019). Design, Implementation and Power Analysis of Pervasive Adaptive Resourceful Smart Lighting and Alerting Devices in Developing Countries Supporting Incandescent and LED Light Bulbs. ncbi.nlm.nih.gov

Safaei, D., Sobhani, A., & Akbar Kiaei, A. (2023). DeePLT: Personalized Lighting Facilitates by Trajectory Prediction of Recognized Residents in the Smart Home. [PDF]

Laidi, R., Djenouri, D., & Ringel, M. (2019). Commercial Technologies for Advanced Light Control in Smart Building Energy Management Systems: A Comparative Study. [PDF]

Kim, T., Lee, G., Hong, J., & Suk, H. J. (2022). Affective Role of the Future Autonomous Vehicle Interior. [PDF]

Naz, A., Kopper, R., P. McMahan, R., & Nadin, M. (2017). Emotional Qualities of VR Space. [PDF]

Hou, Y., Yang, M., Cui, H., Wang, L., Xu, J., & Zeng, W. (2024). C2Ideas: Supporting Creative Interior Color Design Ideation with Large Language Model. [PDF]

Çelen, A., Han, G., Schindler, K., Van Gool, L., Armeni, I., Obukhov, A., & Wang, X. (2024). I-Design: Personalized LLM Interior Designer. [PDF]

Demir, G., Cekmis, A., Bugra Yesilkaynak, V., & Unal, G. (2021). Detecting Visual Design Principles in Art and Architecture through Deep Convolutional Neural Networks. [PDF]

Tsesmelis, T., Hasan, I., Cristani, M., Galasso, F., & Del Bue, A. (2018). RGBD2lux: Dense light intensity estimation with an RGBD sensor. [PDF]

Han, L., Zhang, H., Xiang, Z., Shang, J., Anjani, S., Song, Y., & Vink, P. (2021). Desktop lighting for comfortable use of a computer screen. ncbi.nlm.nih.gov

Stefani, O. & Cajochen, C. (2021). Should We Re-think Regulations and Standards for Lighting at Workplaces? A Practice Review on Existing Lighting Recommendations. ncbi.nlm.nih.gov