关键词: 柑橘皮渣, 柑橘皮渣, 资源化利用, 有机肥

Abstract: Based on the meteorological data both inside and outside the solar greenhouse in Xinzhou, Shanxi province. The minimum and maximum temperature forecast model was established based on BP neural network and stepwise regression model. The result showed that R2 was higher than 0.96 and most of the root mean square error (RMSE) and absolute error (AE) was lower than 2℃ on BP neural network model. The precision of stepwise regression model was higher than BP neural network model in minimum and maximum temperature of clear day and maximum temperature of overcast. The precision of BP neural network model was higher than stepwise regression model in minimum and maximum temperature of cloudy and minimum temperature of overcast. The model was chosen by more precision could predicate extreme temperature in solar greenhouse. The model could provide scientific basis for facility agriculture management and environment regulation and microclimate prediction in Shanxi province.