A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems

• Main Authors: Akhter, Muhammad Naveed, Mekhilef, Saad, Mokhlis, Hazlie, Ali, Raza, Usama, Muhammad, Muhammad, Munir Azam, Mohd Khairuddin, Anis Salwa
• Format: Article
• Published: Elsevier 2022
• Subjects: TK Electrical engineering. Electronics Nuclear engineering
• Online Access: http://eprints.um.edu.my/41479/
• Institution: Universiti Malaya

The integration of photovoltaic energy into a grid demands accurate power output forecasting. In this research, an hour ahead prediction of power output is performed on an annual basis over real data period (2016-2019) for three different PV systems based on polycrystalline, monocrystalline, and thin-film technologies. The solar radiation, ambient temperature, module temperature and wind speed are the considered input parameters, while the power output of each PV system is the output parameter. A hybrid deep learning (DL) method (SSA-RNN-LSTM) is proposed for an hour ahead prediction of output power for each PV system. The proposed technique is compared with GA-RNN-LSTM, PSO-RNN-LSTM and RNN-LSTM. The considered forecasting accuracy measurement parameters are RMSE, MSE, MAE and coefficient of determination (R-2). The findings elaborate that SSA-RNN-LSTM has shown better forecasting accuracy with the lowest (RMSE and MSE), highest (R-2) and highest convergence speed compared to other methods. The proposed model has shown testing (RMSE and MAE) of (19.14% and 21.57%), (15.4% and 10.81%) and (22.9% and 25.2%) lower than RNN-LSTM for polycrystalline, monocrystalline and thin-film PV systems respectively. Furthermore, the proposed model is found more robust in predicting the power output for three different PV systems over four years data period.