991 |
Fast power output prediction for a single row of ducted cross-flow water turbines using a BEM-RANS approach Dominguez F, Achard JL, Zanette J, Corre C Renewable Energy, 89, 658, 2016 |
992 |
Machine learning ensembles for wind power prediction Heinermann J, Kramer O Renewable Energy, 89, 671, 2016 |
993 |
Transfer learning for short-term wind speed prediction with deep neural networks Hu QH, Zhang RJ, Zhou YC Renewable Energy, 85, 83, 2016 |
994 |
Day-ahead forecasting of solar power output from photovoltaic plants in the American Southwest Larson DP, Nonnenmacher L, Coimbra CFM Renewable Energy, 91, 11, 2016 |
995 |
On a universal model for the prediction of the daily global solar radiation Kaplanis S, Kumar J, Kaplani E Renewable Energy, 91, 178, 2016 |
996 |
Hybrid solar irradiance now-casting by fusing Kalman filter and regressor Cheng HY Renewable Energy, 91, 434, 2016 |
997 |
Most influential parametrical and data needs for realistic wind speed prediction Agrawal A, Sandhu KS Renewable Energy, 94, 452, 2016 |
998 |
PV power conversion and short-term forecasting in a tropical, densely-built environment in Singapore Nobre AM, Severiano CA, Karthik S, Kubis M, Zhao L, Martins FR, Pereira EB, Ruther R, Reindl T Renewable Energy, 94, 496, 2016 |
999 |
Developing multiple regression models from the manufacturer's ground-source heat pump catalogue data Simon F, Ordonez J, Reddy TA, Girard A, Muneer T Renewable Energy, 95, 413, 2016 |
1000 |
Short-term wind speed forecasting using empirical mode decomposition and feature selection Zhang C, Wei HK, Zhao JS, Liu TH, Zhu TT, Zhang KJ Renewable Energy, 96, 727, 2016 |