Incremental Auto Regressive Prediction Models with External Variables of Greenhouse Air Temperature for Control Purposes

The impact of actuators should be considered in the prediction modeling of greenhouse air temperature. In this paper, the operating state of a greenhouse was divided into five sub-states based on the on-off characteristic of actuators. A group of novel incremental auto regressive models with external variables (IARX models) suitable for the five operating sub-states were deduced from the mechanistic modeling of greenhouse air temperature. The new IARX models have fewer coefficients than other known ARX models. In order to validate the IARX prediction models, the related environmental factors of a glass greenhouse were measured. The prediction results of the IARX models were compared with two typical ARX models. The maximum prediction errors and the mean square errors of the IARX models, under the three operating sub-states of passive state(all actuators are not working), mechanical ventilation and fan-pad cooling, are 0.1°C, 0.14°C, 0.7°C, and 0°C, 0.3°C, 0.4°C, respectively. The prediction results are much better than those of one compared model, while similar with the other.