南方塑料大棚冬春季温湿度的神经网络模拟

利用浙江省慈溪市草莓塑料大棚和南京信息工程大学农业气象试验站番茄塑料大棚的小气候观测数据及气象站资料,建立3个以棚外辐射、温度、相对湿度和风速为输入变量,棚内温度和相对湿度为输出变量的BP神经网络预测模型。结果表明,3个模型气温训练值与实测值的均方根误差(RMSE)都在2℃以内,相对误差都在4%左右;相对湿度训练值的RMSE都在7个百分点以内,相对误差不超过7%。利用此模型得到的气温预测值与实测值的RMSE都在2℃左右,冬季气温的相对误差较大,春季通风和不通风模型气温的相对误差不超过6%;相对湿度预测值的RMSE都在7个百分点以内,相对误差不超过9%。说明所建BP神经网络模型对于不同季节、不同通风条件、不同作物的大棚温湿度模拟都有较高的精度,能够满足棚内温湿度的预测要求,且对温度的模拟精度高于对相对湿度的模拟。

Neural Network Simulation on Air Temperature and Relative Humidity inside Plastic Greenhouse during Winter and Spring in Southern China

Based on the meteorological data both inside and outside the plastic greenhouse in Cixi, Zhejiang province and agricuhural meteorological experimental station of Nanjing University of Information Science and Technology, three BP neural network models were established, which the input variable was chosen as radiation solar outdoor, air temper- ature, relative humidity and wind speed, and output variable was chosen as temperature indoor and relative humidity. The results showed that all of the root mean square error （RMSE） between trained air temperature and measured value from three models was no more than 2~C and the relative error （RE） no more than 4% respectively. Both RMSE and RE between trained relative humidity and measured value was no more than 7 percent points and 7%. All of the RMSE between predicted air temperature and measured value from three models was 2~C approximately, and their RE was no more than 6% in spring, less than that in winter. RMSE and RE predicated relative humidity and measured value was no more than 7percent points and 9% respectively. The results indicated that three BP neural network models had quite precisely for predicting temperature indoor and relative humidity in plastic greenhouse, which could meet the forecast requirements for plastic greenhouse microclimate.