The control effect of the conventional control method to the greenhouse temperature depends on the accuracy of the plant model and interference measurement. However, an accurate model of the greenhouse is difficult to obtain because of characteristics of the greenhouse such as uncertainty, imprecision, time-varying, multi-disturbance, etc., with the interference being particularly difficult to accurately measure. For example, the conventional PID control algorithm, widely used in many respects with a good performance record, has poor adaptability and weak robustness when used in greenhouses, and the smith predictive control, used in time delay systems to compensate temperature hysteresis, requires precise mathematical object model. Thus, the usual PID+Smith predictor controller often has overshoot and oscillation, generating a large amount of energy consumption in the process of temperature adjustment when used in the greenhouse temperature control system. Therefore, the grey prediction compensation control algorithm is adopted for compensating the aforementioned characteristics of the greenhouse. The advantage of the proposed control strategy is its getting rid of the dependence on conventional control algorithms in the plant model accuracy and interference measurement accuracy, and bypassing the theoretical and technical obstacles in obtaining the object model and interference. Both the simulation and actual operation indicated that the proposed control strategy achieves satisfactory control effect and the system accuracy is significantly improved. Statistical analysis indicated that the correlation coefficients between the estimated value and the true value of the uncertainty and interference grey parameters is 0.9968, 0.9804, and 0.9938, respectively, and the coefficient of determination between them is 0.9935, 0.9585, and 0.9871, respectively. The grey parameters absolute error mean is -0.11510,-0.26733, and-0.31035, and the variance is 0.05150, 0.16324, and 0.09474, the grey parameters relative error mean is-1.68%,-8.06%, and-8.73%, and the variance is 0.01368, 0.00533, and 0.00581. The correlation coefficient between the measured temperature curve and the simulation temperature curve is 0.973972, and the coefficient of determination between them is 0.948621. Also, the overshoot and oscillation in the process of temperature regulation is weakened or eliminated, so the energy consumption is greatly reduced, which not only meets the temperature requirements, but also achieves energy-savings.% 温室温度常规控制方法的控制效果依赖于被控对象模型精确度和干扰测量精确度。而温室系统不确定性、不精确性、时变性和多扰动等特性使温室精确模型很难获得、且干扰很难精确测量。为此，该文采用灰色预测补偿算法对温室对象上述特性进行预测补偿，其优点是可避开温室对象不确定性和干扰因素所带来的在获取对象模型时无法避免的理论和技术上的障碍，摆脱了控制算法对模型精确度和干扰测量精确度的依赖。仿真及实际运行情况均表明，该算法可达到较好的控制效果，控制精度明显提高。统计分析显示，表征不确定性与干扰的灰参量估计值与真值的相关系数分别为0.9968、0.9804、0.9938，决定系数分别为0.9935、0.9585、0.9871；灰参量绝对误差均值为-0.11510、-0.26733、-0.31035，方差为0.05150、0.16324、0.09474，相对误差均值为-1.68%、-8.06%、-8.73%，方差为0.01368、0.00533、0.00581；实测温度曲线与仿真温度曲线相关系数为0.973972，决定系数为0.948621。由于有效地减弱或消除了温度调节过程中的超调和振荡，能耗明显降低，既满足了温度变化的预期要求，又可实现节能。
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