| A 24-h forecast of solar irradiance using artificial neural network: Application for performance prediction of a grid-connected PV plant at Trieste, Italy A Mellit, AM Pavan Solar energy 84 (5), 807-821, 2010 | 1039 | 2010 |
| A novel fault diagnosis technique for photovoltaic systems based on artificial neural networks W Chine, A Mellit, V Lughi, A Malek, G Sulligoi, AM Pavan Renewable Energy 90, 501-512, 2016 | 520 | 2016 |
| A hybrid model (SARIMA–SVM) for short-term power forecasting of a small-scale grid-connected photovoltaic plant M Bouzerdoum, A Mellit, AM Pavan Solar energy 98, 226-235, 2013 | 323 | 2013 |
| The effect of soiling on energy production for large-scale photovoltaic plants AM Pavan, A Mellit, D De Pieri Solar energy 85 (5), 1128-1136, 2011 | 272 | 2011 |
| Fault detection method for grid-connected photovoltaic plants W Chine, A Mellit, AM Pavan, SA Kalogirou Renewable Energy 66, 99-110, 2014 | 216 | 2014 |
| Advanced methods for photovoltaic output power forecasting: A review A Mellit, A Massi Pavan, E Ogliari, S Leva, V Lughi Applied Sciences 10 (2), 487, 2020 | 215 | 2020 |
| Short-term forecasting of power production in a large-scale photovoltaic plant A Mellit, AM Pavan, V Lughi Solar Energy 105, 401-413, 2014 | 200 | 2014 |
| Monitoring, diagnosis, and power forecasting for photovoltaic fields: A review S Daliento, A Chouder, P Guerriero, AM Pavan, A Mellit, R Moeini, ... International Journal of Photoenergy 2017, 2017 | 175 | 2017 |
| Deep learning neural networks for short-term photovoltaic power forecasting A Mellit, AM Pavan, V Lughi Renewable Energy 172, 276-288, 2021 | 173 | 2021 |
| Day-ahead photovoltaic forecasting: A comparison of the most effective techniques A Nespoli, E Ogliari, S Leva, A Massi Pavan, A Mellit, V Lughi, A Dolara Energies 12 (9), 1621, 2019 | 173 | 2019 |
| Least squares support vector machine for short-term prediction of meteorological time series A Mellit, AM Pavan, M Benghanem Theoretical and applied climatology 111, 297-307, 2013 | 172 | 2013 |
| Adaptive Neural Network-Based Control of a Hybrid AC/DC Microgrid AMP Nadjwa Chettibi, Adel Mellit, Giorgio Sulligoi IEEE Transactions on Smart Grid, 2016 | 168 | 2016 |
| An adaptive model for predicting of global, direct and diffuse hourly solar irradiance A Mellit, H Eleuch, M Benghanem, C Elaoun, AM Pavan Energy Conversion and Management 51 (4), 771-782, 2010 | 168 | 2010 |
| FPGA-based implementation of a fuzzy controller (MPPT) for photovoltaic module A Messai, A Mellit, AM Pavan, A Guessoum, H Mekki Energy conversion and management 52 (7), 2695-2704, 2011 | 135 | 2011 |
| Performance prediction of 20 kWp grid-connected photovoltaic plant at Trieste (Italy) using artificial neural network A Mellit, AM Pavan Energy Conversion and Management 51 (12), 2431-2441, 2010 | 118 | 2010 |
| A comparison between BNN and regression polynomial methods for the evaluation of the effect of soiling in large scale photovoltaic plants AM Pavan, A Mellit, D De Pieri, SA Kalogirou Applied energy 108, 392-401, 2013 | 100 | 2013 |
| Power electronic conditioning systems for industrial photovoltaic fields: Centralized or string inverters? AM Pavan, S Castellan, S Quaia, S Roitti, G Sulligoi 2007 International Conference on Clean Electrical Power, 208-214, 2007 | 67 | 2007 |
| A study on the mismatch effect due to the use of different photovoltaic modules classes in large‐scale solar parks A Massi Pavan, A Mellit, D De Pieri, V Lughi Progress in photovoltaics: research and applications 22 (3), 332-345, 2014 | 59 | 2014 |
| An explicit IV model for photovoltaic module technologies N Boutana, A Mellit, S Haddad, A Rabhi, AM Pavan Energy Conversion and Management 138, 400-412, 2017 | 54 | 2017 |
| On-line fault detection of a fuel rod temperature measurement sensor in a nuclear reactor core using ANNs A Messai, A Mellit, I Abdellani, AM Pavan Progress in Nuclear Energy 79, 8-21, 2015 | 53 | 2015 |
Adel MellitRenewable Energy Laboratory , University of Jijel, AlgeriaVerified email at univ-jijel.dz
