Dynamic modeling and control of a direct expansion air conditioning system using artificial neural network

Abstract

An artificial neural network (ANN)-based dynamic model for an experimental variable speed direct expansion (DX) air conditioning (A/C) system has been developed, linking the indoor air temperature and humidity controlled by the DX A/C system with the variations of compressor and supply fan speeds. The values of average relative error ( ARE) and maximum relative error ( MRE) when validating the ANN-based dynamic model developed under three different input patterns were 0.33%, 0.27%, 0.27% and 0.89%, 0.99%, 1.15%, respectively, indicating the high accuracy of the ANN-based dynamic model developed. An ANN-based controller was then developed for controlling the indoor air temperature and humidity simultaneously by varying the compressor speed and supply fan speed in a space served by the experimental DX A/C system. The controllability tests including command following test and disturbance rejection test were carried out using the experimental DX A/C system, and the test results showed that the ANN-based controller developed was able to track the changes in setpoints and to resist the disturbances.