Sci. Tech. Energ. Transition
Volume 78, 2023
|Number of page(s)
|13 December 2023
A state-of-the-art artificial intelligent techniques in daylighting controller: models and performance
Department of Electrical and Electronics Engineering, St. Joseph Engineering College, Mangalore, Karnataka, India
2 Department of Electrical and Electronics Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal 576104, India
* Corresponding author: firstname.lastname@example.org
Accepted: 8 November 2023
Lighting designers are always on the quest to develop a lighting control strategy that is aesthetically pleasing, comfortable, and energy-efficient. In an indoor context, electric lighting blended with daylighting controls forms a quintessential component for improving the occupant’s comfort and energy efficiency. Application of soft computing techniques, adaptive predictive control theory, machine learning, HDR photography, and wireless networking have facilitated recent advances in intelligent building automation systems. The evolution and revolution from the 19th to the 21st century in developing daylighting control schemes and their outcomes are investigated. This review summarizes the state-of-the-art artificial intelligence techniques in daylighting controllers to optimize the performance of conventional photosensor-based control and camera-based control in commercial buildings. The past, current, and future trends are investigated and analyzed to determine the key factors influencing the controller design. This article intends to serve as a comprehensive literature review that would aid in creating promising new concepts in daylighting controllers.
Key words: Daylight-Artificial Light Integrated Scheme / Energy / Automation / Soft computing Techniques / High dynamic range imaging / Window shading
© The Author(s), published by EDP Sciences, 2023
This is an Open Access article distributed under the terms of the Creative Commons Attribution License (https://creativecommons.org/licenses/by/4.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
Current usage metrics show cumulative count of Article Views (full-text article views including HTML views, PDF and ePub downloads, according to the available data) and Abstracts Views on Vision4Press platform.
Data correspond to usage on the plateform after 2015. The current usage metrics is available 48-96 hours after online publication and is updated daily on week days.
Initial download of the metrics may take a while.