A machine learning approach to optimize the performance of a combined solar chimney-photovoltaic thermal power plant

Abstract

Solar Chimney Power Plant (SCPP) is a renewable energy system that indirectly converts solar energy to electricity. However, the efficiency of SCPP is not sufficient for practical applications. Integrating SCPPs with Photovoltaic-Thermal systems (PVT) could enhance their performance to levels acceptable for industrial adoption. This study investigates the combined SCPP-PVT performance for the weather conditions of Austin (Texas), San Diego (California), and Phoenix (Arizona), all on a similar latitude. Various configurations of this combined system are numerically simulated, and their efficiencies are compared with a conventional SCPP, SCPP-Photovoltaic (PV), and stand-alone PV modules. Moreover, to predict and optimize the performance of these systems, the Support Vector Regression with Linear (LSVR), Polynomial (PSVR), Gaussian (GSVR), and Hybrid (HSVR) kernels are implemented. In order to optimize the hyperparameters of the Machine Learning (ML) models, the Grey Wolf Optimizer (GWO) is implemented. Also, the optimum performance of the SCPP-PVT system is obtained using the Multi-objective Grasshopper Optimization Algorithm (MOGOA). The results show that the HSVR ML model has the highest accuracy, followed by PSVR, GSVR, and LSVR models. It is shown that the SCPP-PVT system outperforms both SCPP-PV and stand-alone PV modules, respectively. Finally, the SCPP-PVT is shown to outperform the PV modulus by up to 4.8%.