Evaluation and Analysis on the Temperature Prediction Model for Bailing Mushroom in Jizhou, Tianjin

Based on the air temperature, wind speed, humidity, air pressure, etc., of the regional automatic weather station in Chutouling Town, Jizhou from April 2019 to November 2020, and the air temperature of the microclimate observation station receiving data every 10 min in a bailing mushroom greenhouse, this paper analyzed and evaluated a BP (back propagation) neural network and stepwise regression method to establish a prediction model for the temperature in the Bailing mushroom greenhouse for different seasons. The results showed that: (1) The air temperature, wind speed, humidity and air pressure outside the shed were the main factors for building the temperature prediction model for the inside temperature, and the air temperature was the most important factor affecting the temperature inside the shed. After introducing humidity, wind speed and air pressure, the accuracy of the model was significantly improved. (2) The temperature prediction model based on the BP neural network method, for every 10 min interval in the greenhouse, for the Bailing mushroom in different seasons, was more accurate than the stepwise regression model. The simulation results of the two models had the highest accuracy in summer, followed by autumn. (3) The root means square error of the BP neural network and stepwise regression model for inside the greenhouse, simulating the daily temperature variations for different seasons, was 1.25, 1.10, 1.08, 1.31 °C and 1.29, 1.19, 1.11, 1.37 °C, respectively. The BP neural network method performed better for predicting the daily temperature variations in seasons. (4) The specifying data of high temperature (24 July 2020) and strong cold wave (31 December 2019) were selected to test the two model methods; the results showed that the simulation of the BP neural network model was better than the stepwise regression model.