Building loads consume roughly 40% of the energy produced in developed countries, a significant part of which is invested towards building temperature-control infrastructure. Therein, renewable resource-based microgrids offer a greener and cheaper alternative. This communication explores the possible co-design of microgrid power dispatch and building HVAC (heating, ventilation and air conditioning system) actuations with the objective of effective temperature control under minimised operating cost. For this, we attempt control designs with various levels of abstractions based on information available about microgrid and HVAC system models using the Deep Reinforcement Learning (DRL) technique. We provide control architectures that consider model information ranging from completely determined system models to systems with fully unknown parameter settings and illustrate the advantages of DRL for the design prescriptions.
翻译:建筑负荷消耗发达国家生产的能源的大约40%,其中很大一部分用于建设温度控制基础设施。可再生能源微型电网提供了更绿色、更便宜的替代方法。这一通信探索了微型电网发送和建造HVAC(热电、通风和空调系统)动力装置的可能共同设计,目的是在最低操作成本下有效控制温度。为此,我们试图根据关于利用深强化学习技术的微电网和高VAC系统模型的现有信息,以不同程度的抽取方法控制设计。我们提供了考虑模型信息的控制结构,从完全确定的系统模型到完全未知参数设置的系统,并说明了DRL对设计处方的优势。