为了系统分析日光温室内外气候特征的关系，向日光温室作物环境调控及小气候预报提供支持，根据冬季日光温室内小气候观测试验资料和附近气象站观测资料，利用BP神经网络方法建立3个模型，分别对3种不同天气状况下石家庄地区日光温室冬季小气候特征进行模拟。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，决定系数都在0.95以上；相对湿度训练值的RMSE都在2个百分点以内，决定系数均高于0.95；接受到的太阳辐射的训练值与实测值的RMSE都在16 W/m2以内，决定系数也均超过0.95。利用此模型得到的气温预测值与实测值的RMSE都在2℃以内，决定系数都在0.9以上；相对湿度预测值与实测值的RMSE都在4个百分点以内，晴天和少云-多云状况下决定系数均高于0.9，寡照状况下的决定系数略低，约为0.8；接受到的太阳辐射的预测值与实测值的RMSE都在26 W/m2以内，决定系数均超过0.95。说明所建BP神经网络模型对于不同天气状况下石家庄地区日光温室冬季小气候特征模拟都有较高的精度，可以用于预测。
Abstract
To systematically analyze the relationship of climate characteristics inside and outside the solar greenhouse, and provide support to the solar greenhouse crop environmental regulation and microclimate forecasting, based on the observed data of plastic sunlight greenhouse microclimate and neighboring weather station, by using the method of BP neural network, 3 models were established, to assimilate the microclimate characters under plastic sunlight greenhouse in Shijiazhuang Region during the winter. The results showed that: all of the root mean square error (RMSE) between air temperature trained and measured value from 3 models was no more than 2℃ and the coefficient of determination was more than 0.95 respectively. RMSE between relative humidity trained and measured value was no more than 2 percent points and the coefficient of determination was more than 0.95. RMSE between trained and measured value of solar radiation received was no more than 16 W/m2 and the coefficient of determination was also more than 0.95. All of the RMSE between air temperature predicted and measured value from the 3 models was no more than 2℃ and the coefficient of determination was more than 0.95 respectively. RMSE between relative humidity predicated and measured value was no more than 4 percent points, and their coefficient of determination was more than 0.9 in sunny or slight cloud-cloudy day, more than that which was 0.8 approximately in sunless day. RMSE between predicted and measured value of solar radiation received was no more than 26 W/m2 and the coefficient of determination was more than 0.95. The results indicated that 3 BP neural network models had quite precisely for predicting microclimate characteristics under plastic sunlight greenhouse in different weather conditions in Shijiazhuang Region in winter, which could meet the forecast requirements.
关键词
日光温室； 冬季小气候； BP神经网络； 模拟
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Key words
sunlight greenhouse; microclimate in winter; BP neural network; simulation
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参考文献
[1] Businger J A. The glasshouse climate physics of plant environment [M].Amsterdam: North-Holland Publishing Company,1963:277-318.
[2] Takami S. A model of the greenhouse with a storage-type heat exchanger and its verification[J].Journal of Agricultural Meteorology,1977,33(3):155-165.
[3] Bot G P A. Greenhouse climate: from physical processed to a dynamic model[D].Wageningen, Nearthlands: Agricultural University,1983:1-240.
[4] Stanghellini C, Jong T. A model of humidity and its applications in a greenhouse[J].Agricultural and Forest Meteorology,1995,76(2): 129-148.
[5] 李百军.智能化温室综合环境控制技术的研究[D].镇江:江苏大学, 2002:1-82.
[6] 孟力力,杨其长,Bot G P A,等.日光温室热环境模拟模型的构建[J].农业工程学报,2009,25(1):164-170.
[7] 霍飞,王旭,王双喜.基于黄瓜栽培的塑料连栋温室环境模拟[J].山西农业大学学报:自然科学版,2010,30(3):281-284.
[8] 陈教料,胥芳,张立彬,等.基于热平衡模型的温室地表水源热泵系统供暖设计与试验[J].农业工程学报,2011,27(11):227-231.
[9] 魏瑞江,王西平,常桂荣,等.连阴天气塑料日光温室内外温度的关系及调控[J].中国农业气象,2001,22(3):24-27.
[10] 李军,杨秋珍,吴元中.非加温型四连栋塑料温室内外温湿度关系研究[J].气象,2005,31(8):22-25.
[11] 刘可群,黎明锋,杨文刚.大棚小气候特征以及与大气候的关系[J].气象,2008,34(7):101-107.
[12] 魏瑞江,王春乙,范增禄.石家庄地区日光温室冬季小气候特征及其与大气候的关系[J].气象,2010,36(1):97-102.
[13] 王孝卿,李楠,薛晓萍.寿光日光温室小气候变化规律及模拟方法[J].中国农学通报,2012,28(10):236-242.
[14] Linker R, Seginer I, Gutman P O. Optimal CO2 control in a greenhouse modeled with neural networks[J].Computers and electronics in agriculture,1998,19:289-310.
[15] Ferreira M, Faria E A, Ruano A E. Neural network models in greenhouse air temperature prediction[J].Neurocomputing,2002,43 (1/4):51-75.
[16] Frausto H U, Pieters J G. Modeling greenhouse temperature using system identification by means of neural networks[J]. Neurocomputing,2004,56:423-428.
[17] 汪小旵,丁为民,罗卫红,等.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[J].农业工程学报,2004,20(2): 235-238.
[18] 金志凤,符国槐,黄海静,等.基于BP神经网络的杨梅大棚内气温预测模型研究[J].中国农业气象,2011,32(3):362-367.
[19] 李倩,申双和,曹雯,等.南方塑料大棚冬春季温湿度的神经网络模拟[J].中国农业气象,2012,33(2):190-196.
[20] 何芬,马承伟.遗传算法优化人工神经网络模型在日光温室湿度预报中的应用[J].中国农学通报,2008,24(1):492-495.
[21] 朱春侠,童淑敏,胡景华,等.BP神经网络在日光温室湿度预测中的应用[J].农机化研究,2012(7):207-210.
[22] 中国气象局.地面气象观测规范 [M].北京:气象出版社,2003: 133-134.
[23] 赵苏璇,罗坚,杨成荫.基于 BP神经网络的气象格点数据无损压缩方法[J].地球科学进展,2008,23(2):206-213.
[24] 金龙.人工神经网络技术发展及在大气科学领域的应用[J].气象科技,2004,32(6):385-392.
[25] 胥雪炎,李补喜.不同被解释变量选择对决定系数 R2的影响研究[J].太原科技大学学报,2007,28(5):363-365.
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