The effects of model reduction and data assimilation on greenhouse climate predictions

We investigated the effect of model reduction and data assimilation on prediction accuracy of greenhouse climate. For this, a first-principle model was reduced, and calibrated with measurement data. Calibration data consisted of a time series of temperature, humidity, and carbon-dioxide concentration in a rose greenhouse, together with 15 variables related to outside climate and control actions. The results indicate that model reduction does not produce a crucial loss of prediction accuracy. In contrast, data assimilation decreases the size and variance of prediction errors drastically, making predictions much more reliable. A static linear model seems to predict the most essential input-output response for temperature and humidity, but the predictive power for carbon-dioxide concentration is limited. The prediction errors have standard deviations of typically 2°C, for temperature, 5-10% for relative humidity, and 200-300 ppm for CO₂. The prediction errors have biases of the same order, which differ per period for which the predictions are made. We believe these results are promising for modelling climate via a static black box approach, in combination with data assimilation. The relatively low computational demand for uncertainty analysis and easy model building provide a suitable starting point for investigating augmented systems, such as plant development based on controlled climate.