Intelligent Lighting Control Systems for Energy Savings in Hospital Buildings Using Artificial Neural Networks
DOI:
https://doi.org/10.33003/fjs-2024-0802-2320Keywords:
ANN; Arduino; Energy savings; lighting control systems; microcontrollerAbstract
Lighting control systems are essential in modern building automation and smart homes, efficiently managing illumination to enhance energy conservation and user comfort. This project tackles energy consumption challenges in hospital buildings by introducing Intelligent Lighting Control Systems (ILCS) that take natural light and occupancy into account, driven by Artificial Neural Networks (ANN) and diverse machine learning algorithms. In our study, we collected sensor data, processed it, and designed a lighting control system employing a feedforward neural network and various machine learning algorithms. Surprisingly, our research found that a linear regression algorithm surpassed the ANN-based system in this context. We implemented a prototype, tested it on hardware, and obtained the expected results. This research marks progress towards optimizing energy use in hospital buildings and contributing to sustainability endeavors. By combining ILCS and machine learning, it offers a promising approach for more efficient and eco-friendly lighting systems
References
Adamu, Y., Ali, A. H., Timothy, B. and Mohammad, M. M.(2020). Occurrence of extended spectrum beta lactamase encoding gene among urinary pathogenic Escherichia coli Klebsiella pneumoniae isolates obtained from a tertiary hospital in Gombe, Nigeria. Journal of Bioscience and Medicine. 8:9.
Bassey, E. E., Tarh, J. E., Tu, J. U. And Ekpiken, E. S. (2022). Antibiogram profile of enteric pathogens isolated from fomites in Cross river university of technology medical center, Cabalar, Nigeria. Annual Research and Review in Biology. 21-36.
Published
How to Cite
Issue
Section
FUDMA Journal of Sciences