Study on the effect of wind direction on the thermal performance of cavity receiver for trough solar system: Artificial neural network approach based on genetic algorithm

The structure of the receiver will affect the thermal performance of the trough solar system. Based on the typical characteristics of alpine areas, it is beneficial to analyze the effects of different wind directions and temperatures on the thermal performance of the developed new inverted trapezoidal cavity receiver, to improve the efficiency. In this paper, a comprehensive approach is proposed to establish an indoor controllable experimental platform. Backpropagation (BP) and Genetic Algorithm-Backpropagation (GA-BP) artificial neural networks and Support Vector Regression (SVR) are established, to predict and validate the variation of the heat loss of the inverted trapezoidal cavity receiver. The results show that the heat transfer in the cavity is least perturbed at −45° wind direction. There is a critical value of heat loss in the cavity between 343 K and 363 K, and the thermal performance is moderately improved above the critical temperature. Genetic Algorithm-Backpropagation (GA-BP) performs best in predicting the thermal performance accuracy of the cavity receiver. The four evaluation indexes are 0.021, 0.011, 1.4 %, and 0.002, respectively, which are the lowest, which verifies the effectiveness of the artificial neural network in accurately predicting the thermal performance of the cavity receiver under complex nonlinear conditions.