Selection of Area and Collect Required Data for Power Prediction of a Solar Plant

Abstract

Solar energy is an economical, user-friendly, and practical solution to the global energy dilemma, electricity generates by using solar energy. It can be possible by using two methods- direct method and indirect method. In the direct approach, Photovoltaic modules convert solar irradiation into the form of electrical energy and by indirect technique, concentrated solar power (CSP) collects thermal energy using facilities such as parabolic troughs and Linear Fresnel collectors. To improve the strength of solar power systems, this research focuses on the improvement of accuracy neural network algorithms like NARX and Back Propagation (BP). NARX and BP are utilized for solar energy prediction and solar energy costs can be minimized when improvement is provided to the solar plant efficiently. According to the simulation findings, NARX based neural network increases the solar energy efficiency and better prediction of power when compared to previous or BP methods.