生活热水（DHW）是住宅建筑总能耗的重要组成部分。DHW需求通常表现出与乘客行为密切相关的高度随机行为。在加拿大魁北克市一栋参考40单元住宅楼两年的数据帮助下，对神经网络进行了训练，以预测未来的DHW需求。与静态小时需求计划相比，神经网络提供了更好的预测。然后，将神经网络用于已开发的模型预测控制（MPC）策略，以控制供应给加热器的水流量及其输出温度设定值。在TRNSYS中对热水系统进行了模拟，TRNSYS是一个用于模拟建筑物等瞬态系统的软件，其控制器使用MPC，并在参考建筑物中对基于规则的控制器进行了调整。结果表明，使用MPC时可能会节约能源，这取决于热水系统的设计。 此外，与基于规则的策略相比，使用预测控制器的加热器的能耗在时间上分布更均匀。引用:2019年年度会议，密苏里州堪萨斯城，会议论文

Domestic hot water (DHW) represents an important part of the total energy consumption of residential buildings. DHW demand typically exhibits a highly stochastic behavior strongly linked to occupant behavior. A neural network was trained to predict the future DHW demand with the help of two years of data from a reference 40 unit residential building in Quebec City, Canada. The neural network provided better predictions than a static hourly demand schedule. Then, the neural network was used by a model predictive control (MPC) strategy that has been developed to control the water flow rate supplied to the heater as well as its output temperature setpoint. Simulations of a hot water system were performed in TRNSYS, a software used to simulate transient systems such as buildings, with a controller using the MPC and an adaptation of the rule-based controllers in the reference building. Results show potential energy savings when using the MPC which depends on the hot water system design. Moreover, the energy consumption of the heater using the predictive controller is more uniformly distributed in time than with the rule-based strategy.