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Controlling the environmental parameters, including light in greenhouses,increases the crop yield; however, the electricity cost of supplementallighting can be high. Therefore, the importance of applying cost-effectivelighting methods arises. In this paper, an optimal supplemental lightingcontrol approach is developed considering a variational inference BayesianNeural Network (BNN) model for sunlight prediction. The predictive model isvalidated through testing the model on the historical solar data of a sitelocated at North Carolina ($R^{2}$=0.9971, RMSE=1.8%). The proposed lightingapproach is shown to minimize electricity cost by considering the BNN-basedsunlight prediction, plant light needs, and variable electricity pricing whensolving the underlying optimization problem. For evaluation, the new strategyis compared to: 1) a Markov-based prediction method, which solves the sameoptimization problem, assuming a Markov model for sunlight prediction; 2) aheuristic method which aims to supply a fixed amount of light. Simulationstudies are conducted to examine the electricity cost improvements of theBNN-based approach. The results show that the BNN-based approach reduces costby (on average) 2.27% and 43.91% compared to the Markov prediction-based methodand the heuristic method, respectively, throughout a year.

Optimal Lighting Control in Greenhouses Using Bayesian Neural Networks for Sunlight Prediction