Toward Utilizing IoT Open Data Set to Identify the Room Thermal Comfort

Building sectors are responsible for 33% of global energy consumption and a one-third of CO2 emission as buildings are expected to experience high performance in order to mee occupnt requirments such as lightng, coling, heting, and ventiltion systm. Internet of Things (IoT) as one of the leading developments in digital technologies led to the establishment of devices for improving the living style of the occupants. To date, stdies on intgrating the mechnisms of IoT to identify room thrmal cmfort are very sarce. Therefore, this study discussed the room thermal comfort with respect to room temperature and relative humidity. Three activities i.e. read, write, and sit were adopted. The value of air sped, metablic rate, and clohing inslation was assumed constant. The anlysis was condcted according to Fanger method and ASHRAE standard 55. Center for the Built Environment (CBE) Thermal Comfort Tool was usd to calculate the Predicted Mean Vote (PMV) vales. Results showed the average PMV values of each activity were -2.3 (read), -2.0 (write), and -1.4 (sit). Compared to the room climate data set, sitting performed the closest thermal comfort scale to the neutral. It means light activities with lower metabolic rate should be conducted in the room with higher room temperature and relative humidity