Thermal comfort control based on neural network for HVAC application

This paper describes the design of a thermal comfort controller for indoor thermal environment regulation. In this controller, predicted mean vote (PMV) is adopted as the control objective and six variables are taken into consideration. Meanwhile, a kind of direct neural network (NN) control is designed, and a thermal space model for variable-air-volume (VAV) application is developed. Based on the computer simulation, it is seen that this thermal comfort controller can maintain the indoor comfort level within the desired range under both heating/cooling modes. Furthermore, by combining the energy saving strategy with the VAV application, it also shows the potential for energy saving in future

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