Akhter, Muhammad Naveed and Mekhilef, Saad and Mokhlis, Hazlie and Ali, Raza and Usama, Muhammad and Muhammad, Munir Azam and Mohd Khairuddin, Anis Salwa (2022) A hybrid deep learning method for an hour ahead power output forecasting of three different photovoltaic systems. Applied Energy, 307. ISSN 0306-2619, DOI https://doi.org/10.1016/j.apenergy.2021.118185.
Full text not available from this repository.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.
| Item Type: | Article |
|---|---|
| Funders: | None |
| Uncontrolled Keywords: | An hour ahead power output forecasting; Hybrid deep learning; PV systems; SSA-RNN-LSTM; PSO-RNN-LSTM; GA-RNN-LSTM |
| Subjects: | T Technology > TK Electrical engineering. Electronics Nuclear engineering |
| Divisions: | Faculty of Engineering > Department of Electrical Engineering |
| Depositing User: | Ms. Juhaida Abd Rahim |
| Date Deposited: | 19 Oct 2023 02:54 |
| Last Modified: | 23 Oct 2023 08:52 |
| URI: | http://eprints.um.edu.my/id/eprint/41479 |
Actions (login required)
 |
View Item |
