多联机热泵空调系统智能控制用快速负荷预测模型研究*
摘要：
多联机热泵空调作为集中空调系统的主要形式,其市场份额已超过50%,而基于模型的智能控制技术是提升其运行能效的关键途径。本研究在五大气候区中选取了具有代表性的典型城市,并采用DesignBuilder软件获得各气候区的建筑负荷数据作为研究基础。在建模方法上,本研究结合了物理模型与数据驱动方法:一方面,基于建筑RC(热阻热容)热网络理论构建了4R3C负荷预测物理模型,通过遗传算法实现了冷热负荷最优RC参数的智能辨识,最终获得的均方根误差变异系数(CVRMSE)指标均优于15%;另一方面,基于机器学习理论建立了多层感知机(MLP)数据驱动预测模型,通过系统的训练与测试验证了其预测性能。2种模型的对比研究为多联机热泵空调的模型预测控制提供了坚实的理论基础。
Abstract：
As the main form of central air conditioning system, the VRF heat pump air conditioning system has a market share of more than 50%, and the model-based intelligent control technology is a key way to improve its operational energy efficiency. This study selects representative typical cities in the five climate zones and uses the DesignBuilder software to obtain the building load data of each climate zone as the research basis. In terms of modelling approach, this study combines physical models with data-driven methods. On the one hand, a 4R3C load prediction physical model is constructed based on the theory of building RC thermal network, and the intelligent identification of the optimal RC parameters for heating and cooling loads is realised by the genetic algorithm, and the finally obtained CVRMSE indexes are all better than 15%. On the other hand, the multi-layer perceptron (MLP) data-driven prediction model is established based on the machine learning theory, and its prediction performance is verified through systematic training and testing. The comparative study of the two models provides a solid theoretical foundation for the model predictive control of VRF heat pump air conditioning systems.
Keywords:VRF heat pump air conditioning system; building load; intelligent control; RC thermal network; multi-layer perceptron