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Dynamic modeling and control of a direct expansion air conditioning system using artificial neural network
- Source :
-
Applied Energy . Mar2012, Vol. 91 Issue 1, p290-300. 11p. - Publication Year :
- 2012
-
Abstract
- 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. [Copyright &y& Elsevier]
Details
- Language :
- English
- ISSN :
- 03062619
- Volume :
- 91
- Issue :
- 1
- Database :
- Academic Search Index
- Journal :
- Applied Energy
- Publication Type :
- Academic Journal
- Accession number :
- 67625821
- Full Text :
- https://doi.org/10.1016/j.apenergy.2011.09.037