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Implementation of an Intelligent Model to Predict Solar Energy in North Morocco
| International Journal of Computer Applications |
| Foundation of Computer Science (FCS), NY, USA |
| Volume 184 - Number 5 |
| Year of Publication: 2022 |
| Authors: Chaker El Amrani, Kawtar Chmichi |
10.5120/ijca2022922011
|
Chaker El Amrani, Kawtar Chmichi . Implementation of an Intelligent Model to Predict Solar Energy in North Morocco. International Journal of Computer Applications. 184, 5 ( Mar 2022), 32-36. DOI=10.5120/ijca2022922011
Abstract
Air pollution is mainly due to the use of fossil energy in industrial and transport activities [1,2]. A solution to this problem is to replace fossil fuels by solar energy. This study is about the prediction of solar energy production, in order to decide when and where the switch between the two sources can be made. In this work, different prediction techniques weretested. They were developed with different Machine Learning models, namely, Decision Tree, Random Forest and Neural Networks. The best proposedalgorithmwas implemented in a Web application that shows prediction results, based on environmental variables values.
References
- Chaker El Amrani, Mohamed Akram Zaytar, Gilbert L. Rochon, Tarek El-Ghazawi, "Processing EUMETSAT Big Datasets to Monitor Air Pollution", The European Geosciences Union General Assembly 2018 (EGU), Vienna, Austria, 8–13 April 2018.
- Meryeme Boumahdi, Chaker El Amrani and Siegfried Denys, “An Innovative Air Purification Method and Neural Network Algorithm Applied to Urban Streets”, International Journal of Embedded and Real-Time Communication Systems (IJERTCS), ISSN: 1947-3176, Volume 10, Issue 4, pp. 1-19, September 2019.
- K.N. Nwaigwe, P. Mutabilwa, E. Dintwa, "An overview of solar power (PV systems) integration into electricity grids", Materials Science for Energy Technologies, Volume 2, Issue 3, Pages 629-633, December 2019.
- Christil Pasion, Torrey Wagner, Clay Koschnick, Steven Schuldt, Jada Williams and Kevin Hallinan, "Machine Learning Modeling of Horizontal Photovoltaics Using Weather and Location Data", Energies 13, 2570; doi:10.3390/en13102570, May 2020.
- Pandas Website: https://pandas.pydata.org/
- Abdoul Kader Sidibe, Mounir Ghogho, Chaker El Amrani, Mustapha Faquir, “A comparative study of simple clear sky irradiance models”, 2nd International Conference on Electrical and Information Technologies, Tangier, Morocco, May 4-7, 2016. (http://ieeexplore.ieee.org/document/7519572)
- Cyril Voyant, Gilles Notton, SoterisKalogirou, Marie-Laure Nivet, Christophe Paoli, Fabrice Motte, Alexis Fouilloy, "Machine learning methods for solar radiation forecasting: A review", Renewable Energy, Volume 105, Pages 569-582, May 2017.
- Streamlit Website: https://streamlit.io/
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