Improving HVAC operational efficiency in small-and medium-size commercial buildings
Small- and medium-size (<100,000 sf) commercial buildings (SMBs) represent over 95% of the U.S. commercial building stock and consume over 60% of total site energy consumption. Many of these buildings use rudimentary controls that are mostly manual, with limited scheduling capability, no monitoring, or failure management. Therefore, many of these buildings are operated inefficiently and consume excess energy. SMBs typically use packaged rooftop units (RTUs) that are controlled by an individual thermostat. There is increased urgency to improve the operating efficiency of existing commercial building stock in the United States for many reasons, chief among them being to mitigate the climate change impacts. Studies have shown that managing set points and schedules of the RTUs will result in up to 20% energy and cost savings. Another problem associated with RTUs is short cycling, when an RTU goes through ON and OFF cycles too frequently. Excessive cycling can lead to excessive wear and to premature failure of the compressor or its components. Also, short cycling can result in a significantly decreased average efficiency (up to 10%), even if there are no physical failures in the equipment. Ensuring correct use of the zone set points and eliminating frequent cycling of RTUs thereby leading to persistent building operations can significantly increase the operational efficiency of the SMBs. A growing trend is to use low-cost control infrastructure that can enable scalable and cost-effective intelligent building operations. The work reported in this paper describes two algorithms for detecting the zone set point temperature and RTU cycling rate that can be deployed on the low-cost infrastructure. These algorithms only require the zone temperature data for detection. The algorithms have been tested and validated using field data from a number of RTUs from six buildings in different climate locations. Overall, the algorithms were successful in detecting the set points and ON/OFF cycles accurately using the peak detection technique. The paper describes the two algorithms, results from testing the algorithms using field data, how the algorithms can be used to improve SMBs efficiency, and presents related conclusions.
- Research Organization:
- Pacific Northwest National Lab. (PNNL), Richland, WA (United States)
- Sponsoring Organization:
- USDOE Office of Energy Efficiency and Renewable Energy (EERE)
- DOE Contract Number:
- AC05-76RL01830
- OSTI ID:
- 1358483
- Report Number(s):
- PNNL-SA-121851; BT0310000
- Journal Information:
- Building and Environment, Vol. 120; ISSN 0360-1323
- Publisher:
- Elsevier
- Country of Publication:
- United States
- Language:
- English
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