Prediction of flat-plate collector performance parameters using artificial neural networks

The objective of this work is to use Artificial Neural Networks (ANN) for the prediction of the performance parameters of flat-plate solar collectors. ANNs have been used in diverse applications and they have been shown to be particularly useful in system modeling and system identification. Six ANN models have been developed for the prediction of the standard performance collector equation coefficients, both at wind and no-wind conditions, the incidence angle modifier coefficients at longitudinal and transverse directions, the collector time constant, the collector stagnation temperature and the collector heat capacity. Different networks were used due to the different nature of the input and output required in each case. The data used for the training, testing and validation of the networks were obtained from the LTS database. The results obtained when unknown data were presented to the networks are very satisfactory and indicate that the proposed method can successfully be used for the prediction of the performance parameters of flat-plate solar collectors. The advantages of this approach compared to the conventional testing methods are speed, simplicity, and the capacity of the network to learn from examples. This is done by embedding experiential knowledge in the network.