Advancing Digital Twin development for Controlled-Environment Agriculture: Comparative evaluation of two greenhouse energy models and their readiness for digital twin implementation

• An EnergyPlus and a 5R1C models were validated for a Mediterranean greenhouse. • Quantitative and structural comparison of the models in a Digital Twin perspective. • Development of a framework to assess model robustness and Digital Twin readiness. • The evaluation highlights interoperability and real-time integration potential. • EnergyPlus provides high fidelity, connectivity, and control-system compatibility. Robust energy modelling is essential for optimizing climate control, energy use, and productivity in greenhouses, particularly under the highly variable weather conditions of Mediterranean areas. This study develops and compares two dynamic modelling approaches: a detailed white-box simulation model with EnergyPlus and a reduced-order model based on a 5R1C resistance–capacitance scheme. Both models were calibrated and validated against experimental data acquired from a monitored Venlo-type greenhouse in central Italy, achieving good agreement with measured indoor air temperatures (RMSE 1.52°C for EnergyPlus and 1.57°C for 5R1C). Beyond predictive accuracy, the models were also evaluated for their readiness to be integrated into Digital Twin (DT) architectures, based on a structured framework specifically developed in this work. The results show that EnergyPlus offers superior interoperability and compatibility with advanced control systems, while the 5R1C model, despite its simplified structure, ensures computational agility suitable for rapid simulations and hybrid real-time applications. The comparative analysis also reveals that both models can play complementary roles within a DT environment, combining high-fidelity simulation with lightweight surrogate modelling. Overall, this study provides methodological guidance for selecting robust and scalable energy modelling approaches according to design and operational objectives. By bridging the gap between energy simulation and DT implementation, the proposed framework contributes to advancing digitalized climate control strategies for Controlled-Environment Agriculture, supporting future developments in smart greenhouse management.