The effect of soiling on the performance of solar mirror materials: Experimentation and modeling

Abstract

The main purpose of the current work is to examine and evaluate the impact of outdoor soiling on the performance of a sample concentrating solar power reflector. The exposure period is from April 2019 to October 2019, and during this period the mirror was left without cleaning. Achieved results from the experiment showed that the soiling index increased by almost 63 % with a maximum accumulation of dust on the mirror after approximately 20 weeks of exposure, then, it decreased due to rain event. Modeling the performance of the exposed mirror is also investigated in this paper. Thus, correlations between essential weather parameters (ambient temperature, wind speed, wind direction, relative humidity, rainfall, and dust optical depth) and soiling index are determined to select the most appropriate attributes for predicting daily soiling. The prediction is performed using Multilayer Perceptron Neural Networks approach with the adjustment of hidden layers, hidden neurons, and epoch iterations. Hence, three artificial neural networks configurations are trained, validated, and compared to other methods. The modeling study presented satisfying results, and the adapted structure includes two hidden layers and 20 hidden nodes for each layer. The best achieved performance showed a correlation coefficient of 1 and 0 % for the different calculated statistical metric errors.