中南大学 wangyun

Language : English

汪运

Home

Journal Publications

Research Projects

Books

Patents

Teaching

Honors and Awards

Recommended Ph.D.Supervisor
Recommended MA Supervisor

MOBILE Version

Journal Publications

Current position: Home > Journal Publications

[1]Wang Yun, Song Mengmeng, Zhang Fan, Xu Houhua, Li Yifen, Zhou Shengchao, Zhang Lingjun.A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting.Applied Energy, 2023, 333: 120601.
[2]Runmin Zou, Yun Wang, Yuxin Duan, Jiameng Pang, Fulin Liu, Shakil R. Sheikh.A novel convolutional informer network for deterministic and probabilistic state-of-charge estimation of lithium-ion batteries.Journal of Energy Storage, 2023, 57: 106298.
[3]Yun Wang, Dongran Song, Tuo Chen, Runmin Zou, Fan Zhang, Lingjun Zhang.Ensemble probabilistic wind power forecasting with multi-scale features.Renewable Energy, 2022, 201: 734–751.
[4]Yun Wang, Runmin Zou, Fan Zhang, Houhua Xu, Lingjun Zhang.A deep asymmetric Laplace neural network for deterministic and probabilistic wind power forecasting.Renewable Energy, 2022, 196: 497–517.
[5]Yun Wang, Runmin Zou, Jiazhi Wang, Fulin Liu, Qianyi Liu.An RLL Current Sharing Snubber for Multiple Parallel IGBTs in High Power Applications.IEEE Transactions on Power Electronics, 2022, 37 (7) : 7555–7560.
[6]Rumin Zou, Yun Wang, Mengmeng Song, Ji Wang, Kaifeng Yang, Michael Affenzeller.Deep non-crossing probabilistic wind speed forecasting with multi-scale features.Energy Conversion and Management, 2022, 257: 115433.
[7]Dongran Song, Yun Wang, Junbo Liu, Jian Yang, Mei Su, Xuebing Yang, Lingxiang Huang, Young Hoon Joo.Optimal design of wind turbines on high-altitude sites based on improved Yin-Yang pair optimization.Energy, 2022, 193: 116794.
[8]Yun Wang, Runmin Zou, Fang Liu, Lingjun Zhang, Qianyi Liu.A review of wind speed and wind power forecasting with deep neural networks.Applied Energy, 2021, 304: 117766.
[9]Runmin Zou, Yun Wang, Jiaxin Yang, Fang Liu, Mohamed Essaaidi, Dipti Srinivasan.Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer.Applied Energy, 2021, 304: 117707.
[10]Yun Wang, Yifen Li, Runmin Zou, Dongran Song.Bayesian infinite mixture models for wind speed distribution estimation.Energy Conversion and Management, 2021, 236: 113946.
[11]Yifen Li, Yun Wang, Zhiya Chen, Runmin Zou.Bayesian robust multi-extreme learning machine.Knowledge-Based Systems, 2020, 210: 106468.
[12]Yun Wang, Runmin Zou, Yifen Li, Aoife M. Foley, Dlzar Al kez, Dongran Song, Qinghua Hu, Dipti Srinivasan.Sparse Heteroscedastic Multiple Spline Regression Models for Wind Turbine Power Curve Modeling[J].IEEE Transactions on Sustainable Energy, 2020
[13]Yun Wang, Qinghua Hu, Linhao Li, Aoife M Foley, Dipti Srinivasan.Approaches to wind power curve modeling: A review and discussion.Renewable and Sustainable Energy Reviews, 2019, 116
[14]Yun Wang, Qinghua Hu, Shenglei Pei.Wind Power Curve Modeling with Asymmetric Error Distribution.IEEE Transactions on Sustainable Energy, 2019
[15]Yun Wang, Haibo Wang, Dipti Srinivasan, Qinghua Hu.Robust functional regression for wind speed forecasting based on Sparse Bayesian learning.Renewable Energy, 2019, 132: 43-60.
[16]Yun Wang, Zongxia Xie, Qinghua Hu, Shenghua Xiong.Correlation aware multi-step ahead wind speed forecasting with heteroscedastic multi-kernel learning.Energy Conversion and Management, 2018, 163: 384-406.
[17]Yun Wang, Qinghua Hu, Dipti Srinivasan, Zheng Wang.Wind power curve modeling and wind power forecasting with inconsistent data.IEEE Transactions on Sustainable Energy, 2018, 10 (1) : 16-25.
[18]Yun Wang, Qinghua Hu, Deyu Meng, Pengfei Zhu.Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive r.Applied Energy, 2017, 208: 1097-1112.
[19]Ping Jiang, Yun Wang, Jianzhou Wang.Short-term wind speed forecasting using a hybrid model[J].Energy, 2017, 119: 561-577.
[20]Haibo Wang, Yun Wang, Qinghua Hu.Self-adaptive robust nonlinear regression for unknown noise via mixture of Gaussians.Neurocomputing, 2017, 235: 274-286.

total24 1/2

firstpreviousnext last Page