| 1 |
Hybrid deep neural model for hourly solar irradiance forecasting
Huang X, Li Q, Tai YH, Chen ZQ, Zhang J, Shi JS, Gao BX, Liu WM
Renewable Energy, 171, 1041, 2021 |
| 2 |
Comparative Weibull distribution methods for reliable global solar irradiance assessment in France areas
Kam OM, Noel S, Ramenah H, Kasser P, Tanougast C
Renewable Energy, 165, 194, 2021 |
| 3 |
A regression unsupervised incremental learning algorithm for solar irradiance prediction
Puah BK, Chong LW, Wong YW, Begam KM, Khan N, Juman MA, Rajkumar RK
Renewable Energy, 164, 908, 2021 |
| 4 |
Use of physics to improve solar forecast: Physics-informed persistence models for simultaneously forecasting GHI, DNI, and DHI
Liu WJ, Liu YG, Zhou X, Xie Y, Han YX, Yoo S, Sengupta M
Solar Energy, 215, 252, 2021 |
| 5 |
Description of short circuit current of outdoor photovoltaic modules by multiple regression analysis under various solar irradiance levels
Nakayama K, Tsuji M, Chantana J, Kawano Y, Nishimura T, Hishikawa Y, Minemoto T
Renewable Energy, 147, 895, 2020 |
| 6 |
Significant decrease of photovoltaic power production by aerosols. The case of Santiago de Chile
Del Hoyo M, Rondanelli R, Escobar R
Renewable Energy, 148, 1137, 2020 |
| 7 |
Short term solar irradiance forecasting via a novel evolutionary multi-model framework and performance assessment for sites with no solar irradiance data
Marzouq M, El Fadili H, Zenkouar K, Lakhliai Z, Amouzg M
Renewable Energy, 157, 214, 2020 |
| 8 |
Identification and assessment of sites for solar farms development using GIS and density based clustering technique- A case of Pakistan
Amjad F, Shah LA
Renewable Energy, 155, 761, 2020 |
| 9 |
Comparing various solar irradiance categorization methods - A critique on robustness
Hartmann B
Renewable Energy, 154, 661, 2020 |
| 10 |
Optimizing the inclined field for solar photovoltaic arrays
Rehman NU, Uzair M
Renewable Energy, 153, 280, 2020 |
