Prediction of Photovoltaic (PV) Output Using Artificial Neutral Network (ANN) Based on Ambient Factors

Abstract

This paper is proposed an artificial neural network (ANN) to apply in the system of prediction of power output from photovoltaic (PV) panel system. In order to test the efficiency and reliability of a proposed ANN model experimental output will be comparing with mathematical equation. The objectives of this project are to develop the ANN model that capable of predicting power output. The activation functions using for the hidden layer is hyperbolic tangent. The training algorithm is used Levenberg-Marquardt backpropagation. The meteorology data as input data was obtained from RET screen database in the period from 1st January 2015 until 31st August 2016. There were two locations in Malaysia to be the subject test; Melaka and Kuala Lumpur. From the result, for Melaka, Malaysia the outputs Vm (MAPE = 0.0003% and RMSE = 8.5%) and Im (MAPE = 4.3% and RMSE = 26.8%). Then, for Kuala Lumpur, Malaysia have a less error and good correlation with Vm (RMSE = 0.2%) and Im(MAPE = 0.008% and RMSE = 0.3%). Hence, average power output was high level in January to March for both locations. The conclusion shows that the performance of power output is depending on the level of solar radiation on the day.