在2019年后冠状病毒病时代,专用室外空气系统(DOAS),为建筑物提供100%的室外空气,被广泛认可,因为它可以通过向占用空间提供新鲜的室外空气来确保可接受的室内空气质量。具有适当设计和操作的DOAS可以提供足够的通风和除湿,同时实现能源效率。尽管如此,在确定DOAS的最优控制序列方面,设计人员和操作员在实践中实施的指导有限。因此,在实践中,在DOAS的设计和控制阶段已经认识到许多问题,包括通风和除湿不足,担心过冷,增加送风干球温度,这可能会导致严重的不适和能源浪费。人们一直在努力开发高性能的DOAS控制,以提高能源效率。然而,这种控制通常很复杂,或者难以解释,供建筑设计师和运营商在实践中考虑。在这方面,本文探讨了一种基于仿真的框架,用于生成DOAS的送风温度控制序列,不仅可以确保提高节能潜力,而且可以保证控制逻辑的实施能力。美国能源部具有动态占用概况的原型小学是用整个建筑模拟程序建模的,EnergyPlus。该模型由DOAS组成,该DOAS带有用于通风的排气能量回收系统和用于空间冷却和加热的风机盘管单元。然后,采用遗传算法来找到真正的最佳送风温度控制序列,以最小化加热的能源成本,通风,和空调系统运行。最后,采用决策树从优化中提取规则,以得出DOAS送风温度的可实施操作顺序。总共12周的模拟,包括四周的加热,冷却,和肩膀的季节,分开,在天气条件下对纽约市进行了案例研究。本案例研究确定了基于优化信息规则提取的控制,与传统的基于室外空气温度的复位控制相比,可以节省约13%的能源成本和25%的能源消耗,冷却,和肩膀的季节。值得注意的是,节能主要是通过减少加热能耗来实现的。重要的是,它几乎对应于真正的最优控制结果,这减少了大约14%的能源成本和27%的能源消耗。从结果来看,可以强调的是,优化信息规则提取可以与最优控制一样有效,同时大大降低了控制的复杂性。
In the era of post-Coronavirus Disease 2019, the dedicated outdoor air system (DOAS), which provides 100% outdoor air for the building, is widely acknowledged as it can ensure acceptable indoor air quality by delivering fresh outdoor air to occupied space. The DOAS with a proper design and operation can provide sufficient ventilation and dehumidification while achieving energy efficiency. Nonetheless, there is limited guidance in determining the optimal control sequence of the DOAS for the designers and operators to implement in practice. Accordingly, in practice, a number of issues have been acknowledged in the design and control phases of DOAS, including insufficient ventilation and dehumidification, and increasing supply air dry-bulb temperature in fear of over-cooling, which might cause significant discomfort and energy waste. There have been efforts to develop high-performing DOAS controls for better energy efficiency. However, such controls are often complex, or difficult to interpret, for building designers and operators to consider in practice. In this regard, this paper explores a simulation-based framework for generating a supply air temperature control sequence of the DOAS not only to ensure improved energy-saving potential but also to guarantee the implement-ability of the control logic. The U.S Department of Energy prototype primary school with dynamic occupancy profiles was modeled with a whole building simulation program, EnergyPlus. The model consists of a DOAS with an exhaust air energy recovery system for ventilation and fan-coil units for space cooling and heating. Then, a Genetic Algorithm was adopted to find the true optimal supply air temperature control sequence in terms of minimizing the energy cost of the heating, ventilation, and air conditioning system operation. Lastly, Decision Tree was adopted to extract rules out of the optimums to derive an implementable sequence of operation for the DOAS supply air temperature. A total of 12 week-simulation including four weeks of heating, cooling, and shoulder seasons, separately, under the weather condition of New York City was conducted for the case
study. This case
study identified that the optimization-informed rule extraction-based control, when compared to conventional outdoor air temperature-based reset control, could save about 13% of energy cost and 25% of energy consumption throughout the heating, cooling, and shoulder seasons. It is notable that the energy-saving was mainly achieved by reducing the heating energy consumption. Importantly, it nearly corresponds to the true optimal control result, which reduces approximately 14% of energy cost and 27% of energy consumption. From the results, it can be highlighted that the optimization-informed rule extraction can be as energy effective as the optimal control, while significantly reducing the complexity of the control.