Energy saving potentials of integrating personal thermal comfort models for control of building systems: Comprehensive quantification through combinatorial consideration of influential parameters
Energy saving potentials of integrating personal thermal comfort models for control of building systems: Comprehensive quantification through combinatorial consideration of influential parameters
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DOI：
10.1016/j.apenergy.2020.114882
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发表时间：
2020-06
期刊：
影响因子：
11.2
通讯作者：
Wooyoung Jung;F. Jazizadeh
中科院分区：
文献类型：
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作者：
Wooyoung Jung;F. Jazizadeh
研究提供了将个人热舒适度曲线集成到供暖、通风和空调 (HVAC) 系统控制回路（即舒适驱动控制）的能源效率的证据。然而，也报告了一些能源消耗增加的矛盾案例。针对这些演示的有限性和集中性，在本研究中，我们对舒适驱动控制的能源效率影响进行了全面评估，以（i）了解各种背景因素的影响及其组合效应，（ii）确定受益于个人舒适集成的操作条件。为此，我们提出了一个基于代理的建模框架，并结合 EnergyPlus 模拟。我们考虑了五个潜在影响参数及其组合安排，包括居住者的热舒适特性、不同的多人居住场景、热区居住者数量、控制策略和气候。我们发现，影响最大的因素是居住者热舒适特性的变化（反映在个人热舒适的概率模型中），其次是共享热区域的居住者数量，以及驱动区域内集体设定点的控制策略。在少于 6 名居住者共享的热区中，我们观察到舒适驱动控制的平均能效增益范围在 -3.5% 至 21.4% 之间。考虑到各种个人舒适度和居住人数，单个区域和多个区域的平均（±标准差）节能范围分别为[−3.7 ± 4.8%, 5.3 ± 5.6%]和[−3.1 ± 4.9%, 9.1 ± 5.1%]。在所有多人入住场景中，0.0% 到 96.0% 的组合实现了节能。
Research studies provided evidence on the energy efficiency of integrating personal thermal comfort profiles into the control loop of Heating, Ventilation, and Air-Conditioning (HVAC) systems (i.e., comfort-driven control). However, some conflicting cases with increased energy consumption were also reported. Addressing the limited and focused nature of those demonstrations, in this study, we have presented a comprehensive assessment of theenergy efficiencyimplications of comfort-driven control to (i) understand the impact of a wide range of contextual factors and their combinatorial effect and (ii) identify the operational conditions that benefit from personal comfort integration. In doing so, we have proposed an agent-based modeling framework, coupled with EnergyPlus simulations. We considered five potentially influential parameters and their combinatorial arrangements including occupants’ thermal comfort characteristics, diverse multi-occupancy scenarios, number of occupants in thermal zones, control strategies, and climate. We identified the most influencing factor to be the variations across occupants’ thermal comfort characteristics - reflected in probabilistic models of personal thermal comfort - followed by the number of occupants that share a thermal zone, and the control strategy in driving the collective setpoint in a zone. In thermal zones, shared by fewer than six occupants, we observed potentials for average energy efficiency gain in a range between −3.5% and 21.4% from comfort-driven control. Accounting for a wide range of personal comfort profiles and number of occupants, the average (±standard deviation) energy savings for a single zone and multiple zones were in ranges of [−3.7 ± 4.8%, 5.3 ± 5.6%] and [−3.1 ± 4.9%, 9.1 ± 5.1%], respectively. Across all multi-occupancy scenarios, a range between 0.0% and 96.0% of combinations resulted in energy savings.