| 1 |
Global solar radiation prediction for Makurdi, Nigeria, using neural networks ensemble
Kuhe A, Achirgbenda VT, Agada M
Energy Sources Part A-recovery Utilization and Environmental Effects, 43(11), 1373, 2021 |
| 2 |
Solar output power forecast using an ensemble framework with neural predictors and Bayesian adaptive combination
Raza MQ, Mithulananthan N, Summerfield A
Solar Energy, 166, 226, 2018 |
| 3 |
Deep learning based ensemble approach for probabilistic wind power forecasting
Wang HZ, Li GQ, Wang GB, Peng JC, Jiang H, Liu YT
Applied Energy, 188, 56, 2017 |
| 4 |
Intelligent and robust prediction of short term wind power using genetic programming based ensemble of neural networks
Zameer A, Arshad J, Khan A, Raja MAZ
Energy Conversion and Management, 134, 361, 2017 |
| 5 |
An evolutionary artificial neural network ensemble model for estimating hourly direct normal irradiances from meteosat imagery
Linares-Rodriguez A, Quesada-Ruiz S, Pozo-Vazquez D, Tovar-Pescador J
Energy, 91, 264, 2015 |
| 6 |
An artificial neural network ensemble method for fault diagnosis of proton exchange membrane fuel cell system
Shao M, Zhu XJ, Cao HF, Shen HF
Energy, 67, 268, 2014 |
| 7 |
Solar energy prediction using linear and non-linear regularization models: A study on AMS (American Meteorological Society) 2013-14 Solar Energy Prediction Contest
Aggarwal SK, Saini LM
Energy, 78, 247, 2014 |
| 8 |
An artificial neural network ensemble model for estimating global solar radiation from Meteosat satellite images
Linares-Rodriguez A, Ruiz-Arias JA, Pozo-Vazquez D, Tovar-Pescador J
Energy, 61, 636, 2013 |
| 9 |
A New Selective Neural Network Ensemble Method Based on Error Vectorization and Its Application in High-density Polyethylene (HDPE) Cascade Reaction Process
Zhu QX, Zhao NW, Xu Y
Chinese Journal of Chemical Engineering, 20(6), 1142, 2012 |
| 10 |
A data-driven approach for steam load prediction in buildings
Kusiak A, Li MY, Zhang ZJ
Applied Energy, 87(3), 925, 2010 |
