中文摘要：

地源热泵空调系统已是一种成熟的温室温度节能控制设备,但尚有通过改变运行方式节约能源的空间。为此,针对温室温度纯滞后、非线性、强耦合难以精确建模的特点,引入灰色预测的方法对温室温度进行建模,并设计控制器对地源热泵循环泵进行变频调控。其系统是：设计引入温室温度灰色预测的控制器,根据温度预测值和设定值之差决定地源热泵循环泵的工作频率,以确保系统合理运行,降低运行能耗。由2015年1月15日和2015年1月16日的试验表明,引入灰色预测对地源热泵循环泵进行变频调控比改造前节约了24%的能源。该方法提高了循环泵的控制品质,而且在温室温度适应范围的前提下,较好地达到了节约能源的目的。

英文摘要：

Ground Source Heat Pump（GSHP） air conditioning system has been well applied in greenhouse temperature control, which is mainly used in warming system for greenhouse during the winter. Although GSHP system is a high-efficiency and energy saving system, it still has potential for improvement in the practical application by changing operation mode to save energy. The method changing circulating pump＇s working frequency of GSHP system was proposed in the study. The greenhouse environment characteristics are a combination of great inertia, pure time-delay and nonlinear which make it difficult for us to obtain an accurate mathematical model of the environment. Thus it was not useable for some traditional algorithms such as PID control algorithm. To solve the problem, in this study, we introduced a grey prediction method in temperature modeling process from which we used previous temperature data sequence to forecast future temperature and the predicted future data as feedback value into the controller. As temperature was the only factor for prediction, we chose GM（1,1） as the grey prediction model. To improve the prediction precision, equal dimension and new information method were applied in the modeling process which meant that the latest data took the place of the oldest data with no change in model dimension. Model dimension also influence the prediction precision. The longer dimensions are used, the more accurate prediction can be obtained. However excess dimension can add time and complex on the calculation. To find the most proper model dimension, we compared the different prediction value under diverse dimension and concluded that six was the most suitable one from which the absolute error between actual value and predicted value was 0.34247 and the variance was 0.035974, which was an acceptable precision of grey prediction. According to the difference between the predicted temperature and the setting temperature of the greenhouse, the controller adjusted and decided the frequency of the circulating p