OcAPO: Fine-grained occupancy-aware, empirically-driven PDC control in open-plan, shared workspaces

IF 3 3区 计算机科学 Q2 COMPUTER SCIENCE, INFORMATION SYSTEMS
Anuradha Ravi , Dulaj Sanjaya Weerakoon , Archan Misra
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引用次数: 0

Abstract

Passive Displacement Cooling (PDC) is a relatively recent technology gaining attention as a means of significantly reducing building energy consumption overheads, especially in tropical climates. PDC eliminates the use of mechanical fans, instead using chilled-water heat exchangers to perform convective cooling. In this paper, we identify and characterize the impact of several key parameters affecting occupant comfort in a 1000m2 open-floor area (consisting of multiple zones) of a ZEB (Zero Energy Building) deployed with PDC units and tackle the problem of setting the temperature setpoint of the PDC units to assure occupant thermal comfort and yet conserve energy. We tackle two key practical challenges: (a) the zone-level (i.e., occupant-experienced) temperature differs significantly, depending on occupancy levels, from that measured by the ceiling-mounted thermal sensors that drive the PDC control loop, (b) sparsely deployed sensors are unable to capture the often-significant differences in ambient temperature across neighboring zones. Using extensive real-world coarser-grained measurement data (collected over 60 days under varying occupancy conditions), (a) we first uncover the various parameters that affect the occupant-level ambient temperature, and then (b) devise a trace-based model that helps identify the optimum combination of PDC setpoints, collectively across multiple zones, while accommodating variations in the occupancy levels and weather conditions. Using this trace-based model, our OcAPO system can assure ambient temperature experienced by occupants within a tolerance of 0.3°C. In contrast, the existing approach of occupancy-agnostic, rule-based setpoint control violates this tolerance interval more than 80% of the time. However, this initial model requires unnecessary and continual database lookups and is unable to derive finer-grained setpoints, thereby potentially missing opportunities for additional energy savings. We thus collected data for another 15 days, with finer-grained setpoint control in increments of 0.2 under varying occupancy conditions in the second phase. To determine PDC setpoints efficiently, we subsequently used the empirical data to train a KNN-based regression model. Additional studies on our real-world testbed demonstrate the regressor-based OcAPO approach is able to assure occupant-level ambient temperature within a narrow 0.2°C tolerance. We also demonstrate that the regression version of OcAPO can reduce the opening percentage of PDC valves (an indirect proxy for energy consumption) by 58.9% under low occupancy compared to the trace-based model.

Abstract Image

OcAPO:在开放式共享工作空间中进行细粒度占用感知、经验驱动的 PDC 控制
被动置换冷却(PDC)是一种相对较新的技术,作为一种大幅降低建筑能耗开销的手段,尤其是在热带气候条件下,该技术日益受到关注。PDC 无需使用机械风扇,而是使用冷水热交换器进行对流冷却。在本文中,我们确定并描述了影响使用 PDC 设备的 ZEB(零能耗建筑)1000 平方米开放地板区域(由多个区域组成)中居住舒适度的几个关键参数的影响,并解决了如何设置 PDC 设备温度设定点以确保居住者热舒适度并节约能源的问题。我们解决了两个关键的实际挑战:(a)根据占用水平,区域级(即居住者体验)温度与天花板安装的热传感器测量到的温度存在显著差异,而天花板安装的热传感器可驱动 PDC 控制回路;(b)稀疏部署的传感器无法捕捉相邻区域间环境温度的显著差异。利用广泛的实际粗粒度测量数据(在不同占用条件下收集了 60 天),(a) 我们首先发现了影响占用级环境温度的各种参数,然后 (b) 设计了一个基于轨迹的模型,该模型可帮助确定多个区域的 PDC 设定点的最佳组合,同时适应占用级别和天气条件的变化。利用这一基于轨迹的模型,我们的 OcAPO 系统可以确保居住者所感受到的环境温度在 0.3°C 的容差范围内。相比之下,现有的与占用无关、基于规则的设定点控制方法在 80% 以上的时间里都会违反这个容差范围。然而,这种初始模型需要进行不必要的、持续的数据库查询,而且无法推导出更精细的设定点,因此有可能错失额外的节能机会。因此,我们又收集了 15 天的数据,在第二阶段的不同占用条件下,以 0.2∘的增量进行更精细的设定点控制。为了有效确定 PDC 设定点,我们随后使用经验数据训练了一个基于 KNN 的回归模型。在我们的实际测试平台上进行的其他研究表明,基于回归器的 OcAPO 方法能够确保在 0.2°C 的较小容差范围内保持住户级环境温度。我们还证明,与基于跟踪的模型相比,回归版 OcAPO 可以在低入住率情况下将 PDC 阀门的开启百分比(间接代表能耗)降低 58.9%。
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来源期刊
Pervasive and Mobile Computing
Pervasive and Mobile Computing COMPUTER SCIENCE, INFORMATION SYSTEMS-TELECOMMUNICATIONS
CiteScore
7.70
自引率
2.30%
发文量
80
审稿时长
68 days
期刊介绍: As envisioned by Mark Weiser as early as 1991, pervasive computing systems and services have truly become integral parts of our daily lives. Tremendous developments in a multitude of technologies ranging from personalized and embedded smart devices (e.g., smartphones, sensors, wearables, IoTs, etc.) to ubiquitous connectivity, via a variety of wireless mobile communications and cognitive networking infrastructures, to advanced computing techniques (including edge, fog and cloud) and user-friendly middleware services and platforms have significantly contributed to the unprecedented advances in pervasive and mobile computing. Cutting-edge applications and paradigms have evolved, such as cyber-physical systems and smart environments (e.g., smart city, smart energy, smart transportation, smart healthcare, etc.) that also involve human in the loop through social interactions and participatory and/or mobile crowd sensing, for example. The goal of pervasive computing systems is to improve human experience and quality of life, without explicit awareness of the underlying communications and computing technologies. The Pervasive and Mobile Computing Journal (PMC) is a high-impact, peer-reviewed technical journal that publishes high-quality scientific articles spanning theory and practice, and covering all aspects of pervasive and mobile computing and systems.
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