In this paper, heating and ventilation air conditioning system (HVAC) dataset for a medium-large size building in Romania has been used. It has been collected in one year. Linear state-space model of each air handling unit (AHU) has been estimated using two methods. Recursive extended least squares (RELS) algorithm has been used to estimate the state-space model in one year and seasonal form in the first method. In the second method, a linear time-series neural network has been used for estimating the state-space model in one year form. The constrained distributed model predictive controller has been applied to each AHU state-space model that was estimated before. Every AHU unit energy consumption has been calculated after applying DMPC controller and PI controller on estimated models using simulation results. Results have been compared with energy consumption calculated using the dataset. The mean of energy consumption reduction with 2 approaches is about 36.94%.

在本文中，使用了罗马尼亚中大型建筑的暖通空调系统 (HVAC) 数据集。它已经收集了一年。每个空气处理单元 (AHU) 的线性状态空间模型已使用两种方法进行估计。在第一种方法中，递归扩展最小二乘（RELS）算法已被用于估计一年和季节性形式的状态空间模型。在第二种方法中，线性时间序列神经网络已被用于估计一年形式的状态空间模型。约束分布式模型预测控制器已应用于之前估计的每个 AHU 状态空间模型。在使用仿真结果对估计模型应用 DMPC 控制器和 PI 控制器后，计算了每个 AHU 单位能耗。结果已与使用数据集计算的能耗进行了比较。两种方法的能耗降低平均值约为36.94%。