Analysing Photovoltaic Carport Canopies Power Generation using Artificial Neural Network and Graphical User Interface

Abstract

This study discusses Analyzing Photovoltaic Carport Canopies Power Generation Using Artificial Neural Network. Carports are free-standing buildings having a roof and solar canopies are an increasingly popular way to take benefits of parking and invest in solar power. Many factors could influence the power output of carport canopies such as depends on peak sun hours, temperature, and shading. Therefore, it is important to predict the PV system's optimal power output. This study will analyze the potential of Artificial Neural Network (ANN) with Graphical User Interface (GUI) which will be applied in the system of prediction of power output from photovoltaic (PV) panel system. To be verified efficiency and reliability, the proposed ANN model and GUI experimental output are in comparison with proposed mathematical equations. The novelty of this project is Renewable energy (RE) friendly, predicting solar panel power output and prediction of solar radiation for solar systems by ANN. Therefore, the prediction of solar power generation involving the calculations of parameters such as the weather, sun hours and temperature plays an important role as the solar panel output will not produce according to its rating. The results stated that the total number of PV panels for carport canopies is 1332. A large proportion of the designated car parking areas have been installed with carport canopies are currently about 11 rows of car parking areas. Also, total energy production for one year, which is 438370030.4 kWh. The total assured power output, maximum power output and net array for one year, values are 149178672 kW, 105096892.9 kW and 94587203.3 kW. The design of a GUI and an ANN which can predict solar power output on a daily, monthly, and annually has been successfully designed and developed.