玻璃温室和塑料大棚内逐时气温模拟模型

doi:10.3969/j.issn.1000-6362.2018.10.003

摘要/Abstract

摘要： 2014-2016年在江苏省不同地区选择塑料大棚和玻璃温室进行设施内气温监测，基于设施内日最高和最低气温，采用余弦分段函数、正弦分段函数、正弦-指数分段函数、一次分段函数和神经网络模型分别模拟不同季节和不同天气状况（晴天和阴雨天）下的逐时气温日变化，探究利用室内最高和最低气温模拟计算逐时气温的方法，以及设施内逐时气温日变化规律。结果表明：5种模型均可通过当日最高、最低气温模拟逐时气温变化，其中神经网络模拟精度较高（RMSE=0.69℃），并且受温室类型、天气状况和季节变化的影响较小，普适性较高；正弦-指数分段函数模拟效果最好（RMSE=0.43℃），且受天气和季节的影响较小，但其受温室本身特性和地区的影响较大；余弦分段函数（RMSE=0.85℃）和正弦分段函数（RMSE=0.78℃）模拟效果相近，且受天气和地区的影响；一次分段函数准确度较低（RMSE=0.90℃）且误差变化较大。各方法对塑料大棚内逐时气温的模拟精度均高于玻璃温室。模型模拟精度的季节变化因模型和温室类型有一定差异，但通常情况下，春季和冬季的模拟误差大于秋季，夏季误差最小。

关键词: 塑料大棚, 玻璃温室, 温度模拟, 温室, 气温日变化

Abstract: In 2014-2016, plastic greenhouses and glass greenhouses in different districts of Jiangsu Province were selected for monitoring. Cosine segmentation function, sinusoidal piecewise function, sine-exponential piecewise function, first-order function and neural network model were used to simulate inside hourly temperature in different seasons and different weather conditions (clear day and rainy day). The results showed that all five models can simulate hourly air temperature inside greenhouse through the highest and lowest temperature of the day. The neural network simulation accuracy was the higher (RMSE=0.69℃) and was less affected by the type of greenhouse, weather conditions, and seasonal changes, the universality was higher. The sinusoidal-exponential piecewise function had the best accuracy (RMSE=0.43℃) and was less affected by weather and seasons, but it was affected by the characteristics of the greenhouse itself and the region. The cosine piecewise function (RMSE=0.8℃) and the sinusoidal function (RMSE=0.78℃) had similar simulation results and was affected by the weather and region. The accuracy of a piecewise function is low (RMSE=0.90℃) and the error varies greatly. Models had higher simulation accuracy in plastic greenhouses then it in glass greenhouses. Seasonal variation of the model's simulation accuracy was different between the models and the types of greenhouse, but usually, the simulation error in spring and winter was greater than in autumn, and the error in summer was the smallest.

Key words: Plastic greenhouse, Glass greenhouse, Simulation of air temperature, Greenhouse, Daily variation of temperature

韦婷婷，杨再强，王琳，赵和丽，李佳帅. 玻璃温室和塑料大棚内逐时气温模拟模型[J]. 中国农业气象, 2018, 39(10): 644-655.

WEI Ting-ting, YANG Zai-qiang, WANG Lin, ZHAO He-li, LI Jia-shuai. Simulation Model of Hourly Air Temperature inside Glass Greenhouse and Plastic Greenhouse[J]. Chinese Journal of Agrometeorology, 2018, 39(10): 644-655.

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