| The aim of the present study was to use the artificial neural network (ANN) to simulate the performance of air condition (AC) unit for validation purposes. This helps save time and effort instead of repeating the test for validation. A backpropagation ANN models with multiple hidden layers were trained using 22 input variables and three targets. More than 800 test reports were used to train the ANN model. The input processing functions, neuron sizes, starting values of the weights and biases, layer transfer functions, training functions, and performance evaluation functions were discussed. The uncertainty components associated with the experimental measurements and learning leakage in the ANN model were evaluated. It was found that the ANN model can predict the performance of AC unit under certain experimental conditions using the information provided by the manufacture. However, to achieve a reliable result, the output of the ANN model should be evaluated as an average of at least 50 runs, with reinitiating the starting values of the weights and biases in each run. The model was also used to study the effect of airflow on the performance of the AC and identify the conditions leading to high AC efficiency. The results indicated that under specific conditions, the AC can achieve maximum efficiency without increasing the power input. |
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