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  • 标题:Heliogreenhouse Ground Temperature Forecasting Technology Research
  • 本地全文:下载
  • 作者:Xue Xiaoping ; Li Hongyi ; Li Nan
  • 期刊名称:Advance Journal of Food Science and Technology
  • 印刷版ISSN:2042-4868
  • 电子版ISSN:2042-4876
  • 出版年度:2016
  • 卷号:12
  • 期号:4
  • 页码:161-167
  • DOI:10.19026/ajfst.12.2894
  • 出版社:MAXWELL Science Publication
  • 摘要:According to internal and external meteorological observation data of the heliogreenhouse from the Shandong Laiwu 2007-2010 winter (December to February), under different weather conditions such as sunny, cloudy and overcast sky, the variation characteristics of the ground temperature as well as its relationship with the internal and external meteorological elements were analyzed. By considering the internal and external meteorological elements of the greenhouse as the triggering factors, a forecast model was constructed to predict the 10 cm hourly ground temperature for the next three days. The results showed that due to the impact of heat exchange between air and the soil surface near the greenhouse, during the day, the temperature gradually reduced with the deepening of the soil, while the opposite trend was observed at night; for shallow soil, the diurnal range of the ground temperature was greater than that of the deep soil layer. The daily maximum temperature showed a gradually decreasing trend from the soil surface to the deep layer, while the daily minimum ground temperature showed an increasing trend. According to the forecast model's prediction results of the 10 cm ground temperature for three weather conditions (sunny, cloudy and overcast sky), the overall forecast accuracy was relatively high, among which the forecast accuracy on overcast days was greater than on sunny or cloudy days. Among the three periods of the day, the forecast accuracy for the time periods 00:00-07:00 and 18:00-23:00 was better than that of 08:00-17:00. The RMSE average values of the 10 cm ground temperature for the next first, second and third day were 0.5, 0.6 and 0.8C, respectively and with the increasing prediction time, the forecast accuracy gradually decreased.
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