Comparative analysis of machine learning models of linear Fresnel solar collector

Abstract

Modeling solar plants, like Linear Fresnel Reflectors, plays an essential role in analyzing plant characteristics, assessing overall efficiency, designing appropriate controllers, and optimizing system's operation. Although physical modeling approaches are widely used, their accuracy when compared to real operational data is deficient. In this context, this paper conducts a novel and comprehensive investigation for an existing Linear Fresnel Reflector using six distinct machine learning algorithms namely, Multilayer Perceptron and LSTM Neural Networks, Random Forest, Decision Trees, Extreme Gradient Boosting, and K-Nearest Neighbours to forecast the useable output power of a linear Fresnel solar plant. Datasets between May 2018 and September 2019 with a 30-s time interval from a linear Fresnel plant located in Nicosia, Cyprus are used in this research. Seven distinct statistical metrics, in conjunction with the computation time are employed to assess the performance of the different machine learning models. The objective of this study is to further improve the prediction of Linear Fresnel Reflectors performance using machine learning model, in order to increase modeling reliability on an annual basis. Outcomes reveal that, among the various models considered, K-Nearest Neighbours demonstrated the most optimal performance with a coefficient of determination 98.81 % and a mean absolute percentage error of 1.975 %.