A distributed sensor network for measurement of human thermal comfort feelings

Previous research suggested that real-time measurement of human thermal comfort feelings could enhance the operation of an air-conditioning system. However, there is no such measurement system in the market. The reason is the difficulty in obtaining the real-time values of predicted mean vote (PMV), a well-recognized human thermal comfort index. In this paper, a distributed sensor network was employed to conduct real-time PMV measurement in office environments. Smart sensors were installed at different locations and were connected on the network. They measured the thermal data around an occupant and processed them in order to derive important information. This information was then transferred over the network to facilitate the measurement of PMV values. A laboratory experiment was performed to evaluate the technical feasibility of the approach, in which the prototype of the newly proposed measurement system was tested. An accuracy of ±0.5 deviating from the actual PMV value was found. A case study was then conducted to evaluate the economic impact of the system in an office using computer simulation. The new measurement system was first modeled to compute PMV values in the office. Advanced control algorithms were employed to issue control actions to maintain the thermal environment by using the PMV values from the measurement system. Much better performance in terms of human comfort and energy consumption was achieved. In conclusion, practical real-time PMV measurement was made feasible by using a distributed sensor network, which yielded a better air-conditioning control.