Algorithms and Adaptation Schemes for a Phytotron Digital Twin Using an Evolutionary Heuristic for Parameter Calibration

Abstract

Digital twins (DTs) are increasingly used in controlled-environment agriculture to model microclimates and drive energy-efficient control. However, long-term drift and seasonal variability require continuous recalibration and controller retuning. We develop a self-adaptive DT of a phytotron chamber that combines an MAPE-K loop with an evolutionary heuristic. A genetic algorithm (GA) calibrates the DT parameters against IoT time series and subsequently optimizes heater control settings (two-position, three-position, and proportional modes) subject to comfort constraints on temperature and humidity. Six monitoring intervals (May–June 2025) are used for per-interval calibration and six-fold cross-validation. The calibrated DT reproduces temperature and humidity with high fidelity across unseen intervals: the average cross-validated deviations are 0.27 °C and 7.1%RH (30 transfers). Controller optimization yields cumulative energy savings of 186.54 kWh (3.24%) over six intervals, with per-interval savings ranging from 0.37% to 5.94%. Coupling GA-based DT calibration with model-in-the-loop controller optimization consistently reduces energy use while maintaining microclimate quality, providing a practical pathway for the robust, year-round operation of vertical farms.