A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems. (1st February 2022)

• Record Type: Journal Article
• Authors: Akhter, Muhammad Naveed; Mekhilef, Saad; Mokhlis, Hazlie; Ali, Raza; Usama, Muhammad; Muhammad, Munir Azam; Khairuddin, Anis Salwa Mohd
• Is Part Of: Applied energy. Volume 307(2022)
• Publication Date: 2022-02-01
• Subjects: An hour ahead power output forecasting -- Hybrid deep learning -- PV systems -- SSA-RNN-LSTM -- PSO-RNN-LSTM -- GA-RNN-LSTM; Power (Mechanics) -- Periodicals; Energy conservation -- Periodicals; Energy conversion -- Periodicals; 621.042
• DOI: 10.1016/j.apenergy.2021.118185
• Languages: English
• ISSNs: 0306-2619

Abstract

Highlights: An hour ahead forecasting of power output for three different PV systems. A hybrid deep learning algorithm (SSA-RNN-LSTM) is proposed. The proposed model is better than RNN-LSTM, GA-RNN-LSTM and PSO-RNN-LSTM. The model is robust for three different PV systems over four years data. Abstract: 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.