Development of An Algorithm For Daylight Simulation and Prediction in Buildings Using A Deep Learning Approach

Authors

• Tolulope David Makanju, Department of Electrical and Information Engineering, College of Engineering and Technology, Achievers University Owo, KM 1, Idasen/Uteh Road, Owo, Ondo State, NigeriaFollow
• Matthew Olayinka Oyeleye, Department of Electrical and Electronics Engineering, School of Electrical and Computer Systems, Federal University of Technology, Akure, Ondo State, Nigeria
• Oluwole John Famoriji, Department of Electrical and Electronics Engineering, Faculty of Engineering and Technology, Federal University of Technology and Environmental Sciences, Iyin-Ekiti, Nigeria

Abstract

This paper presents an algorithm for estimating daylight in buildings based on the weather parameters. The algorithm was used to simulate daylight in buildings. The simulated result was used to develop a deep-learning predictive model that can help engineers estimate the daylight that will be appropriate during the design stage of a building. The deep learning approach for the prediction of daylight is based on the recurrent neural network Batch Normalisation-long short-term memory (BN-LSTM) model because daylight is a time-series occurrence. The accuracy of the model is less than 10% for the daylight lux level in the building, indicating that at an early stage of design, daylight in the building can be estimated based on sun angle, outdoor visible radiation, and the building parameters. Daylight can be predicted at an early stage in the design, and appropriate control strategies can be designed to control the artificial lighting to avoid glare and save energy in the workspace.

Recommended Citation

Makanju, Tolulope David; Oyeleye, Matthew Olayinka; and Famoriji, Oluwole John (2026) "Development of An Algorithm For Daylight Simulation and Prediction in Buildings Using A Deep Learning Approach," The Journal of Engineering Research: Vol. 23: Iss. 1, Article 1.
DOI: https://doi.org/10.53540/1726-6742.1322

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