Zamani Vahid, Abtahi Shaghayegh, Li Yong, Chen Yuxiang
Department of Civil and Environmental Engineering, University of Alberta, Edmonton, AB, Canada.
Build Serv Eng Res Technol. 2024 Mar;45(2):135-160. doi: 10.1177/01436244231221254. Epub 2023 Dec 14.
Buildings can have varying heating and cooling set points to take advantage of favorable environmental conditions and low time-of-use rates. To optimize temperature scheduling and energy planning, building energy managements need reliable building thermal models and efficient estimation methods to accurately estimate space heating and cooling supply (or power demand) over a certain period (e.g., 24 h). This accurate estimation capability is vital for performing temperature control strategies. Therefore, the present study used resistor-capacitor (RC) models and unscented Kalman filter (UKF) integrated with nonlinear least square (NLS) to develop a method for precisely estimating heating and cooling supply to control zone temperature. To evaluate the capability of the method, two case studies are conducted. The first case study involves a made-up simple RC model, while the second case study uses monitored data from a single detached house in different scenarios. The capability of the method is evaluated by applying the estimated heating and cooling supply to the RC thermal model and simulated zone temperatures. Then, assess whether the controlled zone's temperature meets the expected temperature or not. The performance evaluation shows that the developed method can accurately estimate the heating and cooling supply, validating its applicability to temperature control objectives.
This research provides a valuable contribution to modern building industry professionals by offering a precise method for estimating heating and cooling supply for temperature control in buildings. By equipping practitioners with an effective tool to optimize energy management, this study addresses a critical aspect of building performance. The practical case studies demonstrate the versatility and applicability of this approach in real-world scenarios. In a world increasingly prioritizing energy efficiency and sustainability, this research empowers professionals to make informed decisions, enhance building performance, and contribute to a greener and more sustainable future, all within a concise and actionable framework.
建筑物可以有不同的供暖和制冷设定点，以利用有利的环境条件和较低的用电费率。为了优化温度调度和能源规划，建筑能源管理需要可靠的建筑热模型和高效的估算方法，以准确估算特定时间段（例如24小时）内的空间供暖和制冷供应量（或电力需求）。这种准确的估算能力对于执行温度控制策略至关重要。因此，本研究使用电阻-电容（RC）模型和无迹卡尔曼滤波器（UKF）并结合非线性最小二乘法（NLS），开发了一种精确估算供暖和制冷供应量以控制区域温度的方法。为了评估该方法的能力，进行了两个案例研究。第一个案例研究涉及一个虚构的简单RC模型，而第二个案例研究使用了一栋独栋房屋在不同场景下的监测数据。通过将估算的供暖和制冷供应量应用于RC热模型和模拟的区域温度来评估该方法的能力。然后，评估受控区域的温度是否符合预期温度。性能评估表明，所开发的方法能够准确估算供暖和制冷供应量，验证了其在温度控制目标方面的适用性。
本研究为现代建筑行业专业人士提供了一项有价值的贡献，即提供了一种精确估算建筑物温度控制所需供暖和制冷供应量的方法。通过为从业者配备优化能源管理的有效工具，本研究解决了建筑性能的一个关键方面。实际案例研究证明了该方法在实际场景中的通用性和适用性。在一个日益重视能源效率和可持续性的世界中，本研究使专业人士能够做出明智的决策，提高建筑性能，并为更绿色、更可持续的未来做出贡献，所有这些都在一个简洁且可操作的框架内。
