Intraseasonal and interseasonal applicability of a neural network model for real-time estimation of the number of air exchanges per hour of a naturally ventilated greenhouse

Abstract

Neural network NN models with environmental data and the extent of ventilator openings as inputs have the potential to estimate the number of air exchanges per hour N in real time of a naturally ventilated greenhouse. In this study, the intraseasonal and interseasonal applicability of an NN model was verified: whether the model trained in a specific period can be applied to different periods of the same and other seasons. First, the effect of data collection periods for model training and test within the same season on the estimation accuracy of N was examined. The estimation accuracy was lowered even though the model was applied to a period immediately following that used for model training. Adjusting the training dataset so that the relative distribution of the temperature difference inside and outside the greenhouse ∆ T approaches the relative distribution of the test dataset improves the estimation accuracy slightly. However, when the model was applied to interseasonal data, such training data adjustments did not improve the estimation accuracy. This indicates that the NN model needs to be further improved for practical use to estimate N of naturally ventilated greenhouses.