| Principal component analysis and machine learning approaches for photovoltaic power prediction: A comparative study S Chahboun, M Maaroufi Applied Sciences 11 (17), 7943, 2021 | 34 | 2021 |
| Performance comparison of support vector regression, random forest and multiple linear regression to forecast the power of photovoltaic panels S Chahboun, M Maaroufi 2021 9th International Renewable and Sustainable Energy Conference (IRSEC), 1-4, 2021 | 15 | 2021 |
| Novel comparison of machine learning techniques for predicting photovoltaic output power S Chahboun, M Maaroufi International Journal of Renewable Energy Research (IJRER) 11 (3), 1205-1214, 2021 | 13 | 2021 |
| Performance comparison of k-nearest neighbor, random forest, and multiple linear regression to predict photovoltaic panels’ power output S Chahboun, M Maaroufi Advances on Smart and Soft Computing: Proceedings of ICACIn 2021, 301-311, 2022 | 10 | 2022 |
| Cubist regression, random forest and support vector regression for solar power prediction S Chahboun, M Maaroufi Journal of Renewable Energies, 65–72-65–72, 2022 | 2 | 2022 |
| Principal component analysis and artificial intelligence approaches for solar photovoltaic power forecasting S Chahboun, M Maaroufi Advances in Principal Component Analysis, 1-12, 2022 | 2 | 2022 |
| Performance Comparison of Multiple Linear Regression and Support Vector Regression for Photovoltaic Panels' Power Forecasting S Chahboun, M Maaroufi ICREATA’21, 124, 0 | | |
