A multi-objective evolutionary algorithm for optimization of dynamic supplemental light based on fluctuating electricity prices

High electricity prices present a significant economic challenge for greenhouse growers, who must balance production costs with crop quality. Existing climate control systems often use fixed lighting schedules, which are inefficient in markets with fluctuating energy prices. This paper introduces and validates a real-time climate control system that utilizes a Multi-Objective Evolutionary Algorithm (MOEA) to optimize supplemental lighting in greenhouses. The system integrates weather forecasts, electricity price prognoses, and local climate data. It uses this data to dynamically balance two conflicting objectives: achieving a target Daily Light Integral (DLI) for the crop and minimizing electricity costs. Across three experiments with various cultivars, the system reduced energy costs by up to 65% compared to standard fixed-rate controls, without significantly compromising the flowering quality of the crops. These findings demonstrate the generalizability of our system, proving it can consistently reduce energy costs without compromising crop quality, a critical step for commercial viability.