Optimal lighting control in greenhouse by incorporating sunlight prediction

• Developed a sunlight prediction method based on a time-variant Markov model. • Devised an optimal prediction-based supplemental lighting method in greenhouses. • Minimized the lighting cost by formulating the underlying problem as a constrained convex optimization problem; • Showed supplemental lighting electricity cost saving of up to 45% during a year. Supplemental lighting is an effective means for increasing greenhouse productivity. Recently, the use of light-emitting diodes (LEDs), capable of precise and quick dimmability, has increased in greenhouses. However, electricity cost of lighting can be significant, and hence, it is necessary to find optimal lighting strategies to minimize electrical lighting cost. In this paper, we model supplemental lighting in the greenhouses equipped with LEDs as a constrained optimization problem, and we aim to minimize electricity cost of supplemental lighting. We consider not only plant daily light integral (DLI) need during its photoperiod but also sunlight prediction and variable electricity pricing in our model. We use Markov chains to model sunlight irradiance and predict it during the day. By taking sunlight prediction information into account, we avoid supplying more light than the crop requires. Therefore, our lighting strategy prepares sufficient light for plant growth while minimizing electricity cost during the day. We propose an algorithm to find optimal supplemental lighting and evaluate its performance through exhaustive simulation studies using a whole year of weather data and compare it to a heuristic method, which aims to supply a fixed photosynthetic photon flux density (PPFD) to plants at each time step during the day. In addition to simulation studies, we also implemented the proposed lighting strategy in a research greenhouse in Athens, GA. Our prediction-based lighting approach shows (on average) over 45% electricity cost reduction compared to the heuristic method throughout the entire year.