| 1 |
Multi-distribution ensemble probabilistic wind power forecasting
Sun MC, Feng C, Zhang J
Renewable Energy, 148, 135, 2020 |
| 2 |
Ensemble solar forecasting using data-driven models with probabilistic post-processing through GAMLSS
Yagli GM, Yang DZ, Srinivasan D
Solar Energy, 208, 612, 2020 |
| 3 |
Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China
Wang L, Lv SX, Zeng YR
Energy, 155, 1013, 2018 |
| 4 |
An adaptive dynamic short-term wind speed forecasting model using secondary decomposition and an improved regularized extreme learning machine
Sun N, Zhou JZ, Chen L, Jia BJ, Tayyab M, Peng T
Energy, 165, 939, 2018 |
| 5 |
Managing electricity price modeling risk via ensemble forecasting: The case of Turkey
Avci E, Ketter W, van Heck E
Energy Policy, 123, 390, 2018 |
| 6 |
A data-driven multi-model methodology with deep feature selection for short-term wind forecasting
Feng C, Cui M, Hodge BM, Zhang J
Applied Energy, 190, 1245, 2017 |
| 7 |
Short-term wind speed and power forecasting using an ensemble of mixture density neural networks
Men ZX, Yee E, Lien FS, Wen DY, Chen YS
Renewable Energy, 87, 203, 2016 |
| 8 |
An application of the ECMWF Ensemble Prediction System for short-term solar power forecasting
Sperati S, Alessandrini S, Delle Monache L
Solar Energy, 133, 437, 2016 |
| 9 |
Ensemble forecasting of solar irradiance by applying a mesoscale meteorological model
Liu YY, Shimada S, Yoshino J, Kobayashi T, Miwa Y, Furuta K
Solar Energy, 136, 597, 2016 |
| 10 |
A comparison between the ECMWF and COSMO Ensemble Prediction Systems applied to short-term wind power forecasting on real data
Alessandrini S, Sperati S, Pinson P
Applied Energy, 107, 271, 2013 |
