Energy Efficient HVAC System with Distributed Sensing and Control

This paper presents our implementation experience in building an energy efficient HVAC system for cooling and air conditioning. The system exercises the "low exergy" theory and leverages high temperature water (18°C) cooling for better energy efficiency. In order to achieve this, the system decomposes the cooling and dehumidification functionalities, and employs decentralized air control for on-demand dehumidification and ventilation. The system comprises two control modules, namely, radiant cooling module and distributed ventilation module, cooperating with each other to provide the HVAC control. Abundant sensors and embedded control devices are customized and instrumented, and we develop a wireless sensor network to support control data exchange among those devices. Our experimental evaluation demonstrates that the system achieves accurate control targets and promptly responses to environment dynamics. The wireless sensor network effectively supports the system needs with long system lifespan. Compared with traditional HVAC systems, our system is of much higher energy efficiency, as measured by the standard Coefficient of Performance (COP) metric.

[1]  Federico Ferrari,et al.  Chaos: versatile and efficient all-to-all data sharing and in-network processing at scale , 2013, SenSys '13.

[2]  Tian He,et al.  Data forwarding in extremely low duty-cycle sensor networks with unreliable communication links , 2007, SenSys '07.

[3]  Miguel Á. Carreira-Perpiñán,et al.  OBSERVE: Occupancy-based system for efficient reduction of HVAC energy , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[4]  Tian He,et al.  On-demand time synchronization with predictable accuracy , 2011, 2011 Proceedings IEEE INFOCOM.

[5]  Philippe Goffin,et al.  Low exergy building systems implementation , 2012 .

[6]  Alberto Cerpa,et al.  Occupancy based demand response HVAC control strategy , 2010, BuildSys '10.

[7]  Alberto Cerpa,et al.  Thermovote: participatory sensing for efficient building HVAC conditioning , 2012, BuildSys@SenSys.

[8]  Polly Huang,et al.  TriopusNet: Automating wireless sensor network deployment and replacement in pipeline monitoring , 2012, 2012 ACM/IEEE 11th International Conference on Information Processing in Sensor Networks (IPSN).

[9]  Kamin Whitehouse,et al.  The smart thermostat: using occupancy sensors to save energy in homes , 2010, SenSys '10.

[10]  Sang Hyuk Son,et al.  ATPC: Adaptive Transmission Power Control for Wireless Sensor Networks , 2016, TOSN.

[11]  Haiyun Luo,et al.  Datalink streaming in wireless sensor networks , 2006, SenSys '06.

[12]  Alberto E. Cerpa,et al.  Energy efficient building environment control strategies using real-time occupancy measurements , 2009, BuildSys '09.

[13]  Yunhao Liu,et al.  CitySee: Urban CO2 monitoring with sensors , 2012, 2012 Proceedings IEEE INFOCOM.

[14]  Lionel M. Ni,et al.  Probabilistic Approach to Provisioning Guaranteed QoS for Distributed Event Detection , 2008, IEEE INFOCOM 2008 - The 27th Conference on Computer Communications.

[15]  Herbert W. Stanford HVAC Water Chillers and Cooling Towers: Fundamentals, Application, and Operation , 2003 .

[16]  Kamin Whitehouse,et al.  Roomzoner: Occupancy-based room-level zoning of a centralized HVAC system , 2013, 2013 ACM/IEEE International Conference on Cyber-Physical Systems (ICCPS).

[17]  David E. Culler,et al.  BOSS: Building Operating System Services , 2013, NSDI.

[18]  Jiming Chen,et al.  Utility-based asynchronous flow control algorithm for wireless sensor networks , 2010, IEEE Journal on Selected Areas in Communications.

[19]  Thomas Weng,et al.  Duty-cycling buildings aggressively: The next frontier in HVAC control , 2011, Proceedings of the 10th ACM/IEEE International Conference on Information Processing in Sensor Networks.

[20]  Rajesh Gupta,et al.  Sentinel: occupancy based HVAC actuation using existing WiFi infrastructure within commercial buildings , 2013, SenSys '13.

[21]  Gregory M. P. O'Hare,et al.  COPOLAN: non-invasive occupancy profiling for preliminary assessment of HVAC fixed timing strategies , 2011, BuildSys '11.

[22]  Wenbo He,et al.  KIPDA: k-indistinguishable privacy-preserving data aggregation in wireless sensor networks , 2011, 2011 Proceedings IEEE INFOCOM.

[23]  P ? ? ? ? ? ? ? % ? ? ? ? , 1991 .

[24]  Barbara Pfeffer,et al.  Refrigeration and Air Conditioning , 1982 .