SOLAR LOCATION ESTIMATION USING LOGSIG BASED ACTIVATION FUNCTION USING ARTIFICIAL NEURAL NETWORK APPROACH

Abstract

Solar panel is one of the renewable energy that can reduce the environmental pollution and have a wide potential of application. The exact solar prediction module will give a big impact on the management of solar power plants and the design of solar energy systems. This paper attempts to find the best Artificial Neural Network (ANN) based logsig transfer function and various training algorithm that can be used to calculate the temperature module (Tm) in Malaysia. This can be done by simulating the collected data of four weather variables which are the ambient temperature (Ta), local wind speed (Vw), solar radiation flux (Gt) and the relative humidity (Rh) into the Neural Network Tool in MATLAB. Three different ANN transfer function and 14 types of training were compared to choose the best method. Finally, an equation for the ANN model will be generated in order to calculate the temperature module based on ambient temperature, local wind speed, solar radiation and relative humidity variables.