Analysis and Research on the Performance of Solar Concentration Based on Big Data and Machine Learning

Empirical formulas for PTC optical efficiency calculation are difficult and costly to obtain from rigorous comparative experiments, whereas simpler optical modeling methods inadequately incorporate realistic optical effects. In this article, algorithms are respectively developed to calculate the geometric concentration ratio (C_g) of linear Cassegrainian solar concentrators (CSC) with a secondary flat mirror based on the way of edge rays from solar sources to a flat-plate receiver. On the basis of the large amount of data generated, machine learning and Python language programming methods are used to analyze and process the data, and the functional relationship between the concentration ratio and each parameter is obtained. The learning and training effect is good, and the ideal result is achieved.