建筑物中的智能能源管理控制系统（EMCS）为降低暖通空调系统的能耗，同时维持或改善室内环境条件提供了一种极好的方法。这可以通过使用计算智能和优化来实现。因此，本文提出并评估了基于进化算法和人工神经网络的暖通空调系统优化过程。该过程可以集成到EMC中，以执行多个智能功能，并实现最佳的整个系统性能。利用从现有暖通空调系统收集的数据对提出的模型和优化过程进行了测试。 测试结果表明，与传统的运行策略相比，该模型可以很好地捕捉系统性能，优化过程可以减少约11%的能耗。引用:ASHRAE Trans。，第118卷第。德克萨斯州圣安东尼奥2号

Intelligent energy management control system (EMCS) in buildings offers an excellent means of reducing energy consumptions in HVAC systems while maintaining or improving indoor environmental conditions. This can be achieved through the use of computational intelligence and optimization. The paper thus proposes and evaluates model-based optimization process for HVAC systems using evolutionary algorithm and artificial neural networks. The process can be integrated into the EMCS to perform several intelligent functions and achieve optimal whole-system performance. the proposed models and the optimization process are tested using data collected from an existing HVAC system. The testing results show that the models can capture very well the system performance and the optimization process can reduced energy consumptions by about 11% when compared to the traditional operating strategies applied.