Choi Youngsik, Lu Xing, O'Neill Zheng, Feng Fan, Yang Tao
J. Mike Walker '66 Department of Mechanical Engineering, Texas A&M University, College Station, TX, United States.
Pacific Northwest National Laboratory, Richland, WA, United States.
Energy Build. 2023 Jun 23:113295. doi: 10.1016/j.enbuild.2023.113295.
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.
在2019冠状病毒病之后的时代，为建筑物提供100%室外空气的独立新风系统（DOAS）得到了广泛认可，因为它可以通过向有人空间输送新鲜室外空气来确保可接受的室内空气质量。设计和运行得当的独立新风系统能够在实现能源效率的同时提供充足的通风和除湿功能。尽管如此，对于设计师和操作人员在实际应用中确定独立新风系统的最佳控制顺序，可供参考的指导却很有限。因此，在实际中，独立新风系统的设计和控制阶段出现了一些问题，包括通风和除湿不足，以及因担心过度冷却而导致送风干球温度升高，这可能会造成严重的不适和能源浪费。人们一直在努力开发高性能的独立新风系统控制方法以提高能源效率。然而，这类控制方法通常很复杂，或者难以理解，建筑设计师和操作人员在实际中难以考虑采用。在这方面，本文探索了一种基于模拟的框架，用于生成独立新风系统的送风温度控制顺序，不仅要确保提高节能潜力，还要保证控制逻辑的可实施性。使用全建筑模拟程序EnergyPlus对具有动态人员占用情况的美国能源部原型小学进行了建模。该模型包括一个带有用于通风的排风能量回收系统的独立新风系统以及用于空间冷却和加热的风机盘管机组。然后，采用遗传算法来寻找真正的最佳送风温度控制顺序，以尽量降低供暖、通风和空调系统运行的能源成本。最后，采用决策树从最优解中提取规则，以得出独立新风系统送风温度的可实施操作顺序。针对该案例研究，在纽约市的天气条件下进行了总共12周的模拟，包括分别为期四周的供暖、供冷和过渡季节模拟。该案例研究表明，与传统的基于室外空气温度的重置控制相比，基于优化信息规则提取的控制在整个供暖、供冷和过渡季节可节省约13%的能源成本和25%的能源消耗。值得注意的是，节能主要是通过降低供暖能源消耗实现的。重要的是，它几乎与真正的最优控制结果相符，后者可降低约14%的能源成本和27%的能源消耗。从结果可以看出，基于优化信息规则提取的控制与最优控制一样节能，同时显著降低了控制的复杂性。
