Comparison of neural network models with aerodynamic and empirical models toward real-time estimation of the number of air exchanges per hour of a naturally ventilated greenhouse

The feasibility of estimating the number of air exchanges per hour (N) of a naturally ventilated greenhouse in real time using neural network (NN) models was evaluated. An aerodynamic (AD) model and an empirical model were also used to compare different types of model. The value of N for an eight-span Venlo-type greenhouse with roof vents containing no plants was measured using the tracer gas method with CO2 for 17 d. An AD model derived from Bernoulli’s principle, an empirical model, and several NN models in which explanatory variables differed were trained and validated with 4,508 data points subjected to 10-fold cross-validation. We first compared the AD model, the empirical model, and two NN models that used the same explanatory variables in the empirical model, with and without wind direction. The mean accuracy (in terms of root mean square error (RMSE) and coefficient of determination (r2) in the relationship between measured and estimated N values) was the highest for the two NN models, followed by the empirical model and AD model. We next compared four NN models. The differences among them included that, as explanatory variable(s), only the difference between inside (Ti) and outside (To) air temperatures or both Ti and To was used and whether solar irradiance (I) was used or not. There was a slight improvement in accuracy when using I, irrespective of how air temperature was handled. The NN models thus tended to exhibit a higher degree of accuracy in estimating N of a naturally ventilated greenhouse than the AD and empirical models considered in this study. For the NN model that performed the best in our comparison, a mean RMSE of 1.08 h−1 and a mean r2 of 0.79 were observed.