Heating, ventilation and air conditioning (HVAC) systems are the most energy-consuming building implements for the improvement of indoor environmental quality (IEQ). We have developed the optimal control strategies for HVAC system to respectively achieve the optimal selections of ventilation rate and supplied air temperature with consideration of energy conservation, through the fast prediction methods by using low-dimensional linear ventilation model (LLVM) based artificial neural network (ANN) and low-dimensional linear temperature model (LLTM) based contribution ratio of indoor climate (CRI_(T)). To be continued for integrated control of multi-parameters, we further developed the fast prediction model for indoor humidity by using low-dimensional linear humidity model (LLHM) and contribution ratio of indoor humidity (CRI(H)), and thermal sensation index (TS) for assessment. CFD was used to construct the prediction database for CO₂, temperature and humidity. Low-dimensional linear models (LLM), including LLVM, LLTM and LLHM, were adopted to expand database for the sake of data storage reduction. Then, coupling with ANN, CRI_(T) and CRI_(H), the distributions of indoor CO₂ concentration, temperature, and humidity were rapidly predicted on the basis of LLVM-based ANN, LLTM-based CRI_(T) and LLHM-based CRI_(H), respectively. Finally, according to the self-defined indices (i.e., E_V, E_T, E_H), the optimal balancing between IEQ (indicated by CO₂ concentration, PMV and TS) and energy consumption (indicated by ventilation rate, supplied air temperature and humidity) were synthetically evaluated. The total HVAC energy consumption could be reduced by 35% on the strength of current control strategies. This work can further contribute to development of the intelligent online control for HVAC systems.

采暖，通风和空调（HVAC）系统是用于改善室内环境质量（IEQ）的最耗能的建筑工具。通过使用基于低维线性通风模型（LLVM）的人工神经网络的快速预测方法，我们已经开发了HVAC系统的最佳控制策略，以在实现节能的同时分别实现通风率和送风温度的最优选择（ ANN）和基于低维线性温度模型（LLTM）的室内气候贡献率（CRI _（T））。为了继续进行多参数的集成控制，我们通过使用低维线性湿度模型（LLHM）和室内湿度的贡献率（CRI（H））以及热敏指数（ TS）进行评估。CFD用于建立CO ₂，温度和湿度的预测数据库。为了减少数据存储量，采用了包括LLVM，LLTM和LLHM的低维线性模型（LLM）来扩展数据库。然后，在基于LLVM的ANN，基于LLTM的CRI _（T）的基础上，结合ANN，CRI _（T）和CRI _（H），快速预测室内CO ₂浓度，温度和湿度的分布。和基于LLHM的CRI _（H）。最后，根据自定义指标（即E _V，E _T，E _H），IEQ（由CO ₂浓度，PMV和TS表示）和能耗（由通风率，供气温度表示）之间的最佳平衡和湿度）进行综合评估。根据当前的控制策略，可以将总的HVAC能耗降低35％。这项工作可以进一步促进HVAC系统的智能在线控制的开发。