利用Elman神经网络的华北棚型日光温室室内环境要素模拟

准确模拟日光温室内环境的变化过程是实现温室环境精准调控的前提.该研究以3个生长季的日光温室室内实时气象观测资料为基础,利用Elman神经网络建模的方法,对日光温室室内1.5 m气温、0.5 m气温和CO2浓度进行逐时模拟,对日光温室室内平均湿度、平均温度、最高温度和最低温度进行逐日模拟,建立基于Elman神经网络的日光...

Simulation of inside environmental factors in solar greenhouses using Elman neural network in North China

Accurate forecast is critical to the hour- and daily-varying changes of environmental factors in different types of structures in a solar greenhouse, particularly to the high effectiveness of greenhouse environment control system. In this study, a 2-year of greenhouse experiment was carried out from 2018 to 2020 in Agricultural Science and Technology Innovation Base, in Wuqing, Tianjin (east longitude 116.97° north latitude 39.43°, altitude 8 m) in north China. Observation data of inside environment factors were used for the solar greenhouse with 6 structural parameters. A hour- and daily-varying model was also constructed with high accuracy. In the hour-varying model, the weather data in No.1 greenhouse were used as modeling data, and the weather data in No.2 greenhouse were used as verification data. In the daily-varying model, the meteorological data in No.3 to No.6 greenhouses were used as modeling data, and the meteorological data in No.1 and No.2 greenhouses were used as verification data. According to the least-squares method, the change range ratio of meteorological factors under different crops was fitted as the crop parameter and the ratio of daily average meteorological factors in different greenhouses as the crop parameter. Elman neural network was used to predict hour-varying inside temperature of 1.5 m, 0.5 m, and CO2 concentration, as well as daily-varying of average humidity, average temperature, the maximum temperature, and minimum temperature in the solar greenhouse. The statistical variables of model validation were also selected to evaluate the accuracy of the model, including the Root Mean Square Error (RMSE), Normalized Root Mean Square Error (NRMSE), and conformity index (D). The prediction results were compared with the stepwise regression and BP neural network modeling. The results showed: 1) In Elman neural network, the RMSE of simulated and measured values for the hour-varying model of inside environmental factors (1.5m air temperature, 0.5 m air temperature, and CO2 concentration) in the solar greenhouse were 2.14℃, 1.33℃, and 55.32 μmol/mol, respectively, while the NRMSE were 10.01%, 5.87%, and 10.70%, respectively. There was optimal stability performance of the hour-varying model for the indoor environment factors in the solar greenhouse. 2) The RMSE of simulated and measured values in the daily-varying model of inside environmental factors (daily average air humidity and temperature, the maximum and minimum air temperature) were 0.59%, 0.88℃, 2.02℃ and 0.98℃, respectively, where the NRMSE were 0.79%, 4.44%, 7.02%, and 6.66%, respectively. It also indicated that the optimal stability of the daily-varying model was achieved. Consequently, the Elman neural network can be expected to accurately simulate the hour- and daily-varying environmental elements. The finding can also provide sound technical support to couple the environmental and crop model in the solar greenhouse.