Greenhouse Climate Modelling
A Hybrid Data Driven and Physics-Based Approach
J.J.G.B. Giesen (TU Delft - Mechanical Engineering)
Tamás Keviczky – Mentor (TU Delft - Team Tamas Keviczky)
More Info
expand_more
Other than for strictly personal use, it is not permitted to download, forward or distribute the text or part of it, without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license such as Creative Commons.
Abstract
The urgent need for sustainable intensification of food production, driven by a growing global
population and increasing climate variability, has positioned greenhouse cultivation and ad
vanced climate control as critical areas of innovation in agriculture. Greenhouses enable pre
cise environmental management, significantly boosting crop yields. However, realizing these
benefits requires climate and crop models that accurately represent greenhouse dynamics
while remaining interpretable and suitable for real-time control.
This thesis addresses greenhouse climate prediction by developing a hybrid modeling approach
combining the strengths of physics-based system identification with modern machine learning
techniques. In collaboration with Hoogendoorn Growth Management, a hybrid model was
developed utilizing a physics-informed Sparse Identification of Nonlinear Dynamics (SINDy)
method to capture primary mechanisms influencing temperature and humidity, augmented
by an Long Short-Term Memory (LSTM) neural network to account for residual, unmodeled
effects. A key contribution of this research is demonstrating the feasibility of transfer learning,
successfully adapting a model trained initially on simulation data to real-world greenhouse
scenarios, yielding reliable predictions for both air temperature and humidity under opera
tional conditions.
Empirical results validate that transfer learning effectively bridges simulation and practi
cal greenhouse environments, underscoring the practicality of data-driven climate models
for industry applications. Integrating the model into an Model Predictive Control (MPC)
framework illustrates its operational viability; the controller accurately tracks temperature
setpoints but experiences challenges maintaining humidity within desired limits. These find
ings emphasize the necessity for accurate predictive models and underscore the importance
of carefully formulating MPC strategies. Further research into modeling humidity dynamics,
expanding the model’s state space, and refining targeted retraining methodologies is essential
to ensure robust, year-round practical deployment.
Overall, this thesis advances greenhouse climate modeling and control by showcasing how hy
brid modeling combined with transfer learning can effectively close the gap between simulation
based development and operational implementation, thereby laying the groundwork for adap
tive and efficient greenhouse management practices