| A review of wind speed and wind power forecasting with deep neural networks Yun Wang, Runmin Zou*, Fang Liu, Lingjun Zhang, Qianyi Liu Applied Energy 304, 117766, 2021 | 505 | 2021 |
| Approaches to wind power curve modeling: A review and discussion Y Wang, Q Hu, L Li, AM Foley, D Srinivasan Renewable and Sustainable Energy Reviews 116, 109422, 2019 | 212 | 2019 |
| The study and application of a novel hybrid forecasting model–A case study of wind speed forecasting in China JZ Wang, Y Wang*, P Jiang Applied Energy 143, 472-488, 2015 | 172 | 2015 |
| Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive robust multi-kernel regression model Y Wang, Q Hu, D Meng, P Zhu Applied energy 208, 1097-1112, 2017 | 139 | 2017 |
| A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China Y Wang, J Wang, X Wei Energy 91, 556-572, 2015 | 138 | 2015 |
| Short-term wind speed forecasting using a hybrid model P Jiang, Y Wang*, J Wang Energy 119, 561-577, 2017 | 133 | 2017 |
| Wind power curve modeling and wind power forecasting with inconsistent data Y Wang, Q Hu, D Srinivasan, Z Wang IEEE Transactions on Sustainable Energy 10 (1), 16-25, 2018 | 121 | 2018 |
| Robust functional regression for wind speed forecasting based on Sparse Bayesian learning Y Wang, H Wang, D Srinivasan, Q Hu Renewable Energy 132, 43-60, 2019 | 72 | 2019 |
| On estimating uncertainty of wind energy with mixture of distributions Q Hu, Y Wang*, Z Xie, P Zhu, D Yu Energy 112, 935-962, 2016 | 57 | 2016 |
| A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting Yun Wang, Houhua Xu, Mengmeng Song, Fan Zhang, Yifen Li, Shengchao Zhou ... Applied Energy 333, 120601, 2023 | 46 | 2023 |
| Correlation aware multi-step ahead wind speed forecasting with heteroscedastic multi-kernel learning Y Wang, Z Xie, Q Hu, S Xiong Energy Conversion and Management 163, 384-406, 2018 | 46 | 2018 |
| A novel convolutional informer network for deterministic and probabilistic state-of-charge estimation of lithium-ion batteries R Zou, Y Duan, Y Wang*, J Pang, F Liu, SR Sheikh Journal of Energy Storage 57, 106298, 2022 | 41 | 2022 |
| Sparse heteroscedastic multiple spline regression models for wind turbine power curve modeling Y Wang, Y Li, R Zou, AM Foley, DA kez, D Song, Q Hu, D Srinivasan IEEE Transactions on Sustainable Energy, 10.1109/TSTE.2020.2988683, 2020 | 39 | 2020 |
| Optimal design of wind turbines on high-altitude sites based on improved Yin-Yang pair optimization D Song, J Liu, J Yang, M Su, Y Wang, X Yang, L Huang, YH Joo Energy 193, 116794, 2020 | 37 | 2020 |
| A deep asymmetric Laplace neural network for deterministic and probabilistic wind power forecasting Yun Wang, Houhua Xu, Runmin Zou, Lingjun Zhang, Fan Zhang Renewable Energy, 2022 | 35 | 2022 |
| Bayesian infinite mixture models for wind speed distribution estimation Y Wang, Y Li, R Zou, D Song Energy Conversion and Management 236, 113946, 2021 | 28 | 2021 |
| Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer Runmin Zou, Jiaxin Yang, Yun Wang∗, Fang Liu, Mohamed Essaaidi, Dipti Srinivasan Applied Energy 304, 117707, 2021 | 28 | 2021 |
| Wind Power Curve Modeling with Asymmetric Error Distribution Y Wang, Q Hu, S Pei IEEE Transactions on Sustainable Energy, 2019 | 28 | 2019 |
| An improved Wavenet network for multi-step-ahead wind energy forecasting Y Wang, T Chen, S Zhou, F Zhang, R Zou, Q Hu Energy Conversion and Management 278, 116709, 2023 | 25 | 2023 |
| Self-adaptive robust nonlinear regression for unknown noise via mixture of Gaussians H Wang, Y Wang*, Q Hu Neurocomputing 235, 274-286, 2017 | 25 | 2017 |
Qinghua HuProfessor of Computer Science, Tianjin University在 tju.edu.cn 的电子邮件经过验证
