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

Ning Li, Liang Xia, Deng Shiming, Xiangguo Xu and Ming-Yin Chan

Applied Energy, 2012, vol. 91, issue 1, 290-300

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.

Keywords: Direct expansion; Air conditioning; Dynamic modeling; Control; Artificial neural network; Variable speed (search for similar items in EconPapers)
Date: 2012
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Citations: View citations in EconPapers (16)

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DOI: 10.1016/j.apenergy.2011.09.037

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