Abstract
This study records the various air conditioning system parameters that affect power consumption and establishes system power consumption models for the chiller, the secondary chilled water pump, the air handling unit (AHU), and the cooling load of the AHU using artificial neural networks. The R2 for each of the models are as high as 0.996. Estimations for the AHU loads in the spaces where the cooling load for the AHU are satisfied and genetic programming is used to find the optimal air conditioning system parameter set for achieving minimum power consumption. These power consumption values are then set as genetic programming end points, and the mathematical symbol (+) is used as the functional ends. Finally, the computational elements of genetic programming are used to perform iterative computation. It may be concluded from the results of the experiment that the optimal parameter set obtained from the genetic programming-based search result in a minimum power consumption that complies with the loading requirements of the location of installation result in a 22% savings in term of power consumption and an average COP increase of approximately 28%, which represent very significant improvements.
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