利用BP神经网络对江淮地区梅雨季节现代化温室小气候的模拟与分析

为对江淮地区现代化温室内梅雨季节的小气候进行模拟与分析，在建立相应的BP神经网络模拟模型的基础上，进一步研究了外部温度、湿度、风速、太阳总辐射和天窗开度5个因素对温室内温度、湿度、风速的影响。研究发现可以使用BP神经网络对梅雨季节的小气候进行模拟，模型具有较高的精度，是对物理模型的有益补充；梅雨季节室内湿度受室外湿度的强烈影响，在5个输入因素中所占比重为51.7%；室内风速主要受室外风速和天窗开度的共同影响，受室外温度的影响较小，所占比重仅为10%；室内温度主要受室外温度和太阳辐射的影响，二者所占比重分别为46.2%和27.9%。

Simulation and analysis of micro-climate of gutter connected Venlo greenhouse during rainy season in Jianghuai region of China using BP neural network

BP neural network was used to simulate greenhouse micro-climate during rainy season in Jianghuai region of China. The effects of outside climate conditions and the degree of ventilation windows opening on greenhouse micro-climate was further analyzed. The results show that greenhouse microclimate during the rainy season can be simulated quite precisely with BP neural network, which can serves as a supplement of physical model for the greenhouse. During the rainy season, air humudity inside greenhouse is mainly affected by outside humidity, accounting up to 51.7% in weight among the five factors. Air flow rate inside greenhouse is mainly affected by outdoor wind speed and ventilation widow openings. Air temperature inside greenhouse is mainly dependent on outdoor temperature and solar radiation. The latter two factors account up to 46.2% and 27.9% in weight, respectively.