基于频域分解的房间冷负荷建模及软测量技术

准确的实时冷负荷是空调运行过程节能的基础,传统的冷负荷计算方法复杂费时,无法满足实时调控的要求。软测量是一种先进的方法,但由于房间冷负荷无法直接测量,因此基于样本数据无法实现软测量模型的参数辨识。针对冷负荷软测量技术的这一瓶颈问题,通过冷负荷的机理分析与建筑能耗仿真软件模拟数据的分析,发现冷负荷在频域中集中分布在以一天为周期的频率点及其二次三次谐波上,利用冷负荷这一频域特性本文提出了一种基于房间温度响应的冷负荷实时软测量技术。该方法通过频域分解,选择不含冷负荷的频域段进行蓄热过程的参数辨识,从而避开了辨识过程对无法实测的实时冷负荷真实值的依赖。基于可测的空调供冷量、房间温度和辨识得到的蓄热系数,就可以利用房间热平衡方程进行冷负荷建模并实现其软测量。通过在建筑能耗仿真平台上的仿真及实际房间的实测实验表明,该方法可以快速有效地实现房间实时冷负荷的软测量,相对于基于机理分析或软件计算的方法更具实用性,同时本方法也为其他难测变量的软测量技术提供了借鉴思路。

Modeling and soft sensing for room cooling load based on frequency domain decomposition

Accurate value of real time room cooling load is the basis data for energy saving in the air conditioning operating process. Unfortunately, traditional calculation methods for room cooling load are too complex and time consuming to meet the demand of real time control. Soft sensing is an attractive technology, however the room cooling load cannot be measured directly, so the parameter identification for the soft sensing model cannot be realized based on the sample data. Aiming at this bottleneck problem in cooling load soft sensing, the mechanism of room cooling load is analyzed and the simulation data from the building energy consumption simulation toolkits are analyzed. It is found that in frequency domain the room cooling load distribution is concentrated on the frequency point with a period of one day and its second harmonic, third harmonic. From this frequency domain characteristic of cooling load, a real time soft sensing technology for room cooling load is proposed based on room temperature response. Through frequency domain decomposition, the parameters of the soft sensing model in the heat storage process were identified in the frequency range without cooling load, so the method avoids the dependence on the real value of un measurable room cooling load. Based on the measureable heat extraction, room air temperature and the identified heat storage coefficient, the room cooling load modeling and soft sensing can be achieved utilizing the room heat balance equation. The simulation on the building energy consumption simulation toolkits and real room measurement experiment show that the proposed method can realize the soft sensing of the room cooling load quickly and efficiently; compared with the traditional calculation methods based on mechanism analysis or software calculation, the proposed method has better practicability, which also provides a reference for the soft sensing of other variables difficult to be measured.