Agbulut, UmitGurel, Ali EtemErgun, AlperCeylan, Ilhan2024-09-292024-09-2920200959-65261879-1786https://doi.org/10.1016/j.jclepro.2020.122269https://hdl.handle.net/20.500.14619/4832This study carried out in two stages. In the first stage, four different-sized layers were designed and manufactured for a concentrated photovoltaic system. These layers were used to change the concentration ratio and area ratio of the system. Furthermore, a new power coefficient equation with this paper is proposed to the literature for the determination of the system performance. In the second stage of the study, the power outputs measured in the study were predicted with four machine-learning algorithms, namely support vector machine, artificial neural network, kernel and nearest-neighbor, and deep learning. To evaluate the success of these machine learning algorithms, coefficient of determination (R-2), root mean squared error (RMSE), mean bias error (MBE), t-statistics (t-stat) and mean absolute bias error (MABE) have been discussed in the paper. The experimental results demonstrated that the double-layer application for the concentrator has ensured better results and enhanced the power by 16%. The average concentration ratio for the double-layer was calculated to be 1.8. Based on these data, the optimum area ratio was determined to be 9 for this V-trough concentrator. Furthermore, the power coefficient was calculated to be 1.35 for optimum area ratio value. R-2 of all algorithms is bigger than 0.96. Support vector machine algorithm has generally presented better prediction results particularly with very satisfying R-2, RMSE, MBE, and MABE of 0.9921, 0.7082 W, 0.3357 W, and 0.6238 W, respectively. Then it is closely followed by kernel-nearest neighbor, artificial neural network, and deep learning algorithms, respectively. In conclusion, this paper is reporting that the proposed new power coefficient approach is giving more reliable results than efficiency data and the power output data of concentrated photovoltaic systems can be highly predicted with the machine learning algorithms. (c) 2020 Elsevier Ltd. All rights reserved.eninfo:eu-repo/semantics/closedAccessCleaner productionConcentratorCPVMachine learning algorithmsPower predictionV-troughArticle10.1016/j.jclepro.2020.1222692-s2.0-85086895804Q1268WOS:000561594800091Q1