Journal
SENSORS
Volume 12, Issue 10, Pages 13458-13470Publisher
MDPI
DOI: 10.3390/s121013458
Keywords
building environment analysis; building energy efficiency; machine learning; smart energy system; occupant comfort
Funding
- Energy Efficiency & Resources of the Korea Institute of Energy Technology Evaluation and Planning (KETEP)
- Ministry of Knowledge Economy, Republic of Korea [2011T100200262]
- Korea Evaluation Institute of Industrial Technology (KEIT) [20112010100020] Funding Source: Korea Institute of Science & Technology Information (KISTI), National Science & Technology Information Service (NTIS)
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In this paper, we propose a new HVAC (heating, ventilation, and air conditioning) control strategy as part of the smart energy system that can balance occupant comfort against building energy consumption using ubiquitous sensing and machine learning technology. We have developed ZigBee-based wireless sensor nodes and collected realistic temperature and humidity data during one month from a laboratory environment. With the collected data, we have established a building environment model using machine learning algorithms, which can be used to assess occupant comfort level. We expect the proposed HVAC control strategy will be able to provide occupants with a consistently comfortable working or home environment.
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