Authors: Moh'd Sami Ashhab; Omar Akash
Addresses: Mechanical and Industrial Engineering Department, American University of Ras Al Khaimah, UAE; Mechanical Engineering Department, Hashemite University, Zarqa, Jordan ' RAK Research and Innovation Center, American University of Ras Al Khaimah, Ras Al Khaimah, UAE
Abstract: Neural networks are utilised to study PV solar panels' performance dependence on accumulated dust and tilt angle. For every location in the world there is an optimum tilt angle of dust-free PV panels for best performance. However, in some areas of the world dust is an obstacle for PV panels operation. Examples of such areas are the Middle East and the Arabian Gulf countries. Accumulation rate of dust depends on the location as well as the tilt angle of the PV panel. In this paper, relevant data are collected for two identical sets of PV panels in the city of Ras Al Khaimah in UAE where significant amounts of dust are present. Each set includes four different PV panels with various tilt angles. One set is kept clean while the other set is left unclean. The data includes information about PV panel cleaning condition, tilt angle, date and output power. Available data are used to train a neural network model which predicts the PV panel power under the given conditions. Furthermore, the neural network model serves as a key for adjusting the tilt angle of the PV panels at an optimum value for best performance taking into account the dust factor.
Keywords: neural networks; optimisation; PV panels; tilt angle; dust.
International Journal of Embedded Systems, 2019 Vol.11 No.1, pp.38 - 45
Available online: 22 Jan 2019 *Full-text access for editors Access for subscribers Free access Comment on this article