张掖日光温室最低温度预报模型的主成分回归法构建

为了有效预报日光温室内部最低温度，减弱低温冷害对设施农业生产的影响。利用张掖日光温室内小气候数据及室外气象观测资料，选取8个影响日光温室最低温度的气象因子进行相关分析和统计检验的多重共线性诊断，应用主成分回归方法建立日光温室最低温度预报模型，并用模型模拟值与温室最低温度实测值比较对模型精度进行检验。结果表明：气象因子X₁、X₂、X₄、X₆、X₇、X₈间存在共线性的问题。通过主成分分析综合提取了3个主成分代替原来的8个变量，建立的温室最低温度预报模型通过α=0.01水平显著性检验，且模型精度检验表明，不同天气条件（晴天、少云—多云、阴天）的模拟值和实测值间R²在0.81~0.89之间，RMSE在0.90~1.16℃之间；不同时段（12月—次年2月）的模拟值和实测值间R²在0.82~0.89之间，RMSE在0.94~1.13℃之间。

Modeling of the Lowest Temperature Forecast in the Sunlight Greenhouse in Zhangye Based on Principal Component Regression

The paper aims to forecast the lowest temperature in the sunlight greenhouse effectively, reduce impact of chilling damage on facility agriculture production. Based on the data of sunlight greenhouse microclimate and meteorological observation outside the greenhouse, 8 meteorological factors which affected the lowest temperature in the sunlight greenhouse were selected and diagnosed through correlation and statistic test. Then a model for forecasting the lowest temperature of sunlight greenhouse was established by using of principal component regression method. The accuracy of model was verified by comparing model simulated values and actual values of the lowest temperature inside greenhouse. The results showed that collinearity existed among X₁, X₂, X₄, X₆, X₇ and X₈. The former 3 principal components could stand for 8 variables, and the regression equation passed the significance test (α=0.01). The regression coefficients properties of principal component regression equation were consistent with the results of correlation analysis, which made the unreasonable symbols of regression coefficients in the least square estimation reasonable. Model precision analysis showed that the R² was 0.81-0.89 and the RMSE was 0.90-1.16℃ under different weather conditions; the R² was 0.82-0.89 and the RMSE was 0.94-1.13℃ at different times.