Performance comparison of artificial intelligence techniques in short term current forecasting for photovoltaic system

Muhammad Murtadha Othman, Mohammad Fazrul Ashraf Mohd Fazil, Mohd Hafez Hilmi Harun, Ismail Musirin, Shahril Irwan Sulaiman


This paper presents artificial intelligence approach of artificial neural network (ANN) and random forest (RF) that used to perform short-term photovoltaic (PV) output current forecasting (STPCF) for the next 24-hours. The input data for ANN and RF is consists of multiple time lags of hourly solar irradiance, temperature, hour, power and current to determine the movement pattern of data that have been denoised by using wavelet decomposition. The Levenberg-Marquardt optimization technique is used as a back-propagation algorithm for ANN and the bagging based bootstrapping technique is used in the RF to improve the results of forecasting. The information of PV output current is obtained from Green Energy Research (GERC) University Technology Mara Shah Alam, Malaysia and is used as the case study in estimation of PV output current for the next 24-hours. The results have shown that both proposed techniques are able to perform forecasting of future hourly PV output current with less error.

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