Short term solar irradiance forecasting using sky images based on a hybrid CNN–MLP model
| Autor: | Abdellatif Ghennioui, Omaima El Alani, Fatima-ezzahra Dahr, Mounir Abraim, Hicham Ghennioui, Ilyass Ikenbi |
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| Rok vydání: | 2021 |
| Předmět: |
Artificial intelligence
Short term forecasting Meteorology Mean squared error business.industry media_common.quotation_subject Irradiance Solar irradiance Solar energy TK1-9971 General Energy Overcast Photovoltaics Sky Sky images Multilayer perceptron Environmental science Electrical engineering. Electronics. Nuclear engineering business media_common |
| Zdroj: | Energy Reports, Vol 7, Iss, Pp 888-900 (2021) |
| ISSN: | 2352-4847 |
| DOI: | 10.1016/j.egyr.2021.07.053 |
| Popis: | High penetration of photovoltaics (PV) has been observed in the energy market over the last decade. However, its integration into electrical grids is challenging, as solar energy is highly fluctuating given its dependence on different weather variables. Consequently, short-term forecasting of solar irradiance provides a pivotal solution to ensure optimal use of the produced energy and reduce its uncertainty. This study proposes a hybrid convolutional neural network and Multilayer perceptron (CNN–MLP) model to forecast the global irradiance 15 min ahead. The model uses images from a hemispherical sky imager, time series of GHI, and weather variables collected from a ground meteorological station in Morocco. The evaluation of the proposed model under clear, mixed, and overcast days shows that the proposed model performs better than the persistence model. The root mean square error (RMSE) varies between 13.05 W/m2 and 49.16 W/m2 for CNN–MLP and between 45.76 W/m2 and 114.19 W/m2 for persistence. The coefficient of determination (R2) varies between 0.99 and 0.94 for the MLP–CNN and between 0.98 and 0.79 for persistence. The results show that the proposed model could be an appropriate choice for short-term forecasting even under cloudy conditions. |
| Databáze: | OpenAIRE |
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