Coupling convolutional neural networks with gated recurrent units to model illuminance distribution from light pipe systems

Existing methods used to predict the performance of light pipe systems depend on empirical models based on specific conditions (e.g., climate, configuration). As a result, they are not readily applicable to conditions outside the initially considered boundaries. We propose machine learning (ML) models as an alternative to existing empirical models. 147 ML models were trained to predict illuminance distribution from a light pipe. Three ML algorithms were considered – convolutional neural networks (CNN), gated recurrent units (GRU) and an ensemble of CNN + GRU. The CNN + GRU model (R² = 0.987) showed a higher predictive performance than the GRU model (R² = 0.981). Additionally, the CNN + GRU model required less time to train and was significantly lighter than the GRU model; these attributes make the ensemble CNN + GRU model preferable during deployment. The developed models also offer better generalization capabilities than existing empirical models and are easily subjected to continued learning as more data becomes available.