Real-time adaptation of a greenhouse microclimate model using an online parameter estimator based on a bat algorithm variant

Greenhouse microclimate modelling is a difficult task mainly due to the strong nonlinearity of the phenomenon and the uncertainty of the involved physical and non-physical parameters. The uncertainty stems from the fact that the majority of these parameters are unmeasurable or difficult to be measured and some of them are time-varying, signifying the necessity to estimate them. In this paper, a methodology for online parameter estimation is proposed to deal with the estimation of the time-varying parameters of a simplified greenhouse temperature model for real-time model adaptation purposes. An online estimator is developed based on an enhanced variant of the Bat Algorithm called the Random Scaling-based Bat Algorithm. It allows the continuous adaptation of the internal air temperature model and the internal solar radiation sub-model, through estimating their parameters at the same time step by minimizing a cost function, intending to achieve global optimality. Constraints on the search ranges are imposed to respect the physical sense. The adaptation of the models was tested with recorded datasets of different agri-seasons and on a real greenhouse in real time. The evolutions of the time-varying parameters were graphically presented and thoroughly discussed. The experimental results illustrate the successful model adaptation, presenting an average error of less than 0.28 degrees C for air temperature prediction and 20Wm(-2) for solar radiation simulation. This proves the usefulness of the proposed methodology under changing environmental conditions.