ABSTRACT

Heating, ventilation and air conditioning (HVAC) systems play an essential role in demand response (DR) programs. In this paper, a fuzzy controller is designed to adjust the HVAC set-points optimally. The aims of the designed controller are multifold: to save energy, to improve user’s comfort, and reduce HVAC electricity costs. In the current research, indices such as daily energy cost, minimum and maximum home temperature, energy usage, energy usage during peak hours, and user’s comfort are proposed and discussed for the evaluation of HVAC function. In addition, the effect of different pricing schemes such as fixed pricing (FP), time-of-use pricing (TOU), and real-time pricing (RTP), are analyzed. Further, the adaptability of the proposed model enabled us to investigate users with different attitudes toward welfare and cost. Finally, the effects of set-point and dead-band width are discussed. The results show that the proposed controller reaches the pre-determined aims successfully.

Abbreviations: HVAC: Heating, ventilation, and air conditioning; FP: Fixed Pricing; TOU: Time Of Use; RTP: Real Time Pricing; PR: The HVAC system energy consumption cost in a day; UC: The total time that the user experiences an uncomfortable situation in a day; T_min: The minimum RealFeel Temperature experienced in a day by the user; T_max: The maximum RealFeel Temperature experienced in a day by the user; ECP: Energy consumed by the HVAC system during peak hours; EC: Energy consumed by the HVAC system during a day

摘要

加热，通风和空调（HVAC）系统在需求响应（DR）计划中起着至关重要的作用。在本文中，设计了模糊控制器来最佳地调节HVAC设定点。设计的控制器的目的是多重的：节省能源，提高用户的舒适度并降低HVAC的电费。在当前的研究中，提出并讨论了诸如每日能源成本，最低和最高家庭温度，能源使用量，高峰时段的能源使用量以及用户的舒适度等指标，以评估HVAC功能。此外，还分析了不同定价方案的影响，例如固定定价（FP），使用时间定价（TOU）和实时定价（RTP）。此外，所提出模型的适应性使我们能够调查对福利和成本持不同态度的用户。最后，讨论了设定点和死区宽度的影响。结果表明，所提出的控制器能够成功达到预定目标。

缩写： HVAC：暖气，通风和空调；FP：固定定价；TOU：使用时间；RTP：实时定价；PR：暖通空调系统一天的能耗成本；UC：用户一天中遇到不适的总时间；T_min：用户一天中遇到的最低RealFeel温度；T_max：用户一天中遇到的最高RealFeel温度；ECP：HVAC系统在高峰时段消耗的能量；EC：一天中HVAC系统消耗的能量