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.

中文翻译：

---

神经网络模型的季节内和季节间适用性，用于实时估算自然通风温室每小时的空气交换量

具有环境数据和通风机开度作为输入的神经网络（NN）模型具有潜在的潜力，可以实时估算自然通风温室每小时的空气交换量（N）。在这项研究中，验证了NN模型的季节内和季节间适用性：在特定时期训练的模型是否可以应用于相同季节和其他季节的不同时期。首先，在同一季节内进行模型训练和测试的数据收集时间对N的估计准确性的影响被检查了。即使将模型应用于紧接模型训练所使用的时间之后，估计准确性也会降低。调整训练数据集，使温室内外温度差（ΔT）的相对分布接近测试数据集的相对分布，可以稍微提高估计精度。但是，当将模型应用于季节间数据时，这种训练数据调整不会提高估计准确性。这表明需要对NN模型进行进一步改进，以用于实际估算自然通风温室的 N。