Optimal Lighting Control in Greenhouses Equipped with High-intensity Discharge Lamps Using Reinforcement Learning

Supplemental lighting in greenhouses has contributed to an improvement in crop growth. However, lighting costs account for a large portion of greenhouse expenses; as a result, it is important to find optimal lighting strategies to minimize supplemental lighting costs. Although light-emitting diodes (LEDs) with precise and quick dimmability are becoming a popular choice for greenhouse supplemental lighting, other common types of horticultural lights, such as high-intensity discharge (HID) lamps, are still used in greenhouses. In this work, we formulate the supplemental lighting control problem in greenhouses equipped with HID lamps as a discrete constrained optimization problem. We aim to minimize electricity costs of supplemental lighting considering sunlight prediction, plant light needs, and variable electricity pricing in our model. By combining Q-learning and method of multipliers, we determine the optimal solution of this discrete optimization problem and then evaluate its performance through exhaustive simulation studies using a whole year of data for a site located at West Virginia. Compared to a heuristic method, which supplies a fixed minimum photosynthetic photon flux density (PPFD) to plants at each time step during the day, the proposed strategy shows about 44% (on average) electricity cost reduction throughout the year.