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个人简介         中南大学副教授、博士研究生导师、硕士研究生导师，湖南省优青，入选2023年全球前2%顶尖科学家榜单（World's Top 2% Scientists）年度科学影响力排行榜。2012年本科毕业于安徽大学经济学院，2015年硕士毕业于兰州大学数学与统计学院，2019年毕业于天津大学智能与计算学部。2017年至2018年于新加坡国立大学Dipti Srinivasan教授的Centre for Green Energy Management & Smart Grid (GEMS)实验室联合培养。        主持1项国家自然科学基金面上项目，1项国家自然科学基金青年项目，1项湖南省优秀青年项目，1项湖南省自然科学基金青年项目，1项重点实验室开放基金，参与/承担多项国家级科研项目。发表JCR1区论文20余篇（https://scholar.google.com.hk/citations?user=HfLiXM4AAAAJ&hl=en），申请发明专利8项，授权发明专利3项。目前担任Renewable and Sustainable Energy Reviews, IEEE Transactions on Sustainable Energy, Applied Energy, Knowledge-Based Systems, Energy Conversion and Management, Renewable Energy等国际期刊审稿人。        此外，长期与Dipti Srinivasan教授实验室保持良好的合作关系，有机会可以推荐同学去新加坡国立大学进行交流学习。 招收：每年2-3名优秀硕士生，进入团队的同学会手把手带，欢迎感兴趣的同学联系我！（团队也比较适合有上博士计划的同学积累科研成果） 具体的研究方向为：不确定性人工智能（深度学习，机器学习），主要应用领域为（1）风速/风功率预测（2）光伏预测（3）电池SOC和SOH估计/预测（4）其他人工智能方向，如故障诊断，环境监测等。 目前指导的研究生及相关科研成果： 杨佳欣，已毕业（22年6月），已于能源领域顶级JCR1区期刊Applied Energy发表学术论文1篇，受理发明专利一项，毕业去向：华为 段雨欣，已毕业（23年6月），已于JCR1区期刊Journal of Energy Storage发表学术论文1篇，毕业去向：中车 宋萌萌，已毕业（23年6月），已于能源领域顶级JCR1区期刊Energy Conversion and Management发表学术论文1篇，受理发明专利1项，毕业去向：哈工大读博 陈远洋，已毕业（23年6月），受理发明专利1项，发表EI会议论文1篇，已投论文至JCR2区期刊1篇，毕业去向：比亚迪 段小聪，研三，已于能源领域顶级JCR1区期刊IEEE Transaction on Sustainable Energy和Energy发表学术论文2篇，获2023年国家奖学金 陈托，研三，已于能源领域顶级JCR1区期刊Renewable Energy和Energy Conversion and Management发表学术论文2篇，获2023年福达奖学金 徐厚华，研三，已于能源领域顶级JCR1区期刊Renewable Energy发表学术论文1篇，在投JCR1区（Renewable and Sustainable Energy Reviews）论文1篇，获2022年“地磁暴智能警报”挑战赛第一名 吴广，研二，研究不确定性引导的光伏预测方法 周芊，研二，研究基于特征多阶关联的电池寿命估计方法 李宇豪，研一，研究电池SOC估计方法 张子洋，研一，研究电池SOH估计方法 已发表论文： [27] Yun Wang, Xiaocong Duan, Dongran Song, Runmin Zou, Fan Zhang, and Yifen Li, “Wind power curve modeling with large-scale generalized kernel-based regression model,” IEEE Transactions on Sustainable Energy, vol. 14, no. 8, pp. 1-12, May 2023, doi: 10.1109/TSTE.2023.3276906. [26] Yun Wang, Xiaocong Duan, Runmin Zou, Fan Zhang, Yifen Li, and Qinghua Hu, “A novel data-driven deep learning approach for wind turbine power curve modeling,” Energy, vol. 270, pp. 1-13, May 2023, doi: 10.1016/j.energy.2023.126908. [25] Yun Wang, Tuo Chen, Shengchao Zhou, Fan Zhang, Ruming Zou, and Qinghua Hu, “An improved wavenet network for multi-step-ahead wind energy forecasting,” Energy Conversion and Management, vol. 278, pp. 1-21, Feb. 2023, doi: 10.1016/j.enconman.2023.116709. [24] Yun Wang, Houhua Xu, Mengmeng Song, Fan Zhang, Yifen Li, Shengchao Zhou, and Lingjun Zhang, “A convolutional Transformer-based truncated Gaussian density network with data denoising for wind speed forecasting,” Applied Energy, vol. 333, pp. 1-21, Mar. 2023, doi: 10.1016/j.apenergy.2022.120601. [23]   Runmin Zou, Yuxin Duan, Yun Wang*, Jiameng Pang, Fulin Liu, and Shakil R. Sheikh, “A novel convolutional informer network for deterministic and probabilistic state-of-charge estimation of lithium-ion batteries,” Journal of Energy Storage, vol. 57, p. 106298, Jan. 2023, doi: 10.1016/j.est.2022.106298. [22]   Yun Wang, Tuo Chen, Runmin Zou, Dongran Song, Fan Zhang, and Lingjun Zhang, “Ensemble probabilistic wind power forecasting with multi-scale features,” Renewable Energy, vol. 201, pp. 734–751, Dec. 2022, doi: 10.1016/j.renene.2022.10.122. [21]   Yun Wang, Houhua Xu, Runmin Zou, Lingjun Zhang, and Fan Zhang, “A deep asymmetric Laplace neural network for deterministic and probabilistic wind power forecasting,” Renewable Energy, vol. 196, pp. 497–517, Aug. 2022, doi: 10.1016/j.renene.2022.07.009. [20]   Runmin Zou, Mengmeng Song, Yun Wang*, Ji Wang, Kaifeng Yang, and Michael Affenzeller, “Deep non-crossing probabilistic wind speed forecasting with multi-scale features,” Energy Conversion and Management, vol. 257, p. 115433, Apr. 2022, doi: 10.1016/j.enconman.2022.115433. [19]   Yun Wang, Jiazhi Wang, Fulin Liu, Qianyi Liu, and Runmin Zou, “An RLL Current Sharing Snubber for Multiple Parallel IGBTs in High Power Applications,” IEEE Transactions on Power Electronics, vol. 37, no. 7, pp. 7555–7560, Jul. 2022, doi: 10.1109/TPEL.2022.3148266. [18]   Yun Wang, Yifen Li, Runmin Zou, and Dongran Song, “Bayesian infinite mixture models for wind speed distribution estimation,” Energy Conversion and Management, vol. 236, p. 113946, May 2021, doi: 10.1016/j.enconman.2021.113946. [17]   Runmin Zou, Jiaxin Yang, Yun Wang*, Fang Liu, Mohamed Essaaidi, and Dipti Srinivasan, “Wind turbine power curve modeling using an asymmetric error characteristic-based loss function and a hybrid intelligent optimizer,” Applied Energy, vol. 304, p. 117707, Dec. 2021, doi: 10.1016/j.apenergy.2021.117707. [16]   Yun Wang, Runmin Zou, Fang Liu, Lingjun Zhang, and Qianyi Liu, “A review of wind speed and wind power forecasting with deep neural networks,” Applied Energy, vol. 304, p. 117766, Dec. 2021, doi: 10.1016/j.apenergy.2021.117766. [15]   Yifen Li, Yun Wang*, Zhiya Chen, and Runmin Zou, “Bayesian robust multi-extreme learning machine,” Knowledge-Based Systems, vol. 210, p. 106468, Dec. 2020, doi: 10.1016/j.knosys.2020.106468. [14] Yun Wang, Yifen Li, Runmin Zou, Aoife M. Foley, Dlzar Al Kez, Dongran Song, Qinghua Hu, and Dipti Srinivasan, “Sparse Heteroscedastic Multiple Spline Regression Models for Wind Turbine Power Curve Modeling,” IEEE Transactions on Sustainable Energy, vol. 12, no. 1, pp. 191–201, Jan. 2021, doi: 10.1109/TSTE.2020.2988683. [13] Dongran Song, Junbo Liu, Jian Yang, Mei Su, Yun Wang*, Xuebing Yang, Lingxiang Huang, and Young Hoon Joo, “Optimal design of wind turbines on high-altitude sites based on improved Yin-Yang pair optimization,” Energy, vol. 193, p. 116794, Feb. 2020, doi: 10.1016/j.energy.2019.116794. [12] Yun Wang, Qinghua Hu, Linhao Li, Aoife M. Foley, and Dipti Srinivasan, “Approaches to wind power curve modeling: A review and discussion,” Renewable and Sustainable Energy Reviews, vol. 116, p. 109422, Dec. 2019, doi: 10.1016/j.rser.2019.109422. [11] Yun Wang, Qinghua Hu, and Shenglei Pei, “Wind Power Curve Modeling With Asymmetric Error Distribution,” IEEE Transactions on Sustainable Energy, vol. 11, no. 3, pp. 1199–1209, Jul. 2020, doi: 10.1109/TSTE.2019.2920386. [10] Yun Wang, Haibo Wang, Dipti Srinivasan, and Qinghua Hu, “Robust functional regression for wind speed forecasting based on Sparse Bayesian learning,” Renewable Energy, vol. 132, pp. 43–60, Mar. 2019, doi: 10.1016/j.renene.2018.07.083. [9] Yun Wang, Zongxia Xie, Qinghua Hu, and Shenghua Xiong, “Correlation aware multi-step ahead wind speed forecasting with heteroscedastic multi-kernel learning,” Energy Conversion and Management, vol. 163, pp. 384–406, May 2018, doi: 10.1016/j.enconman.2018.02.034. [8] Yun Wang, Qinghua Hu, Dipti Srinivasan, and Zheng Wang, “Wind Power Curve Modeling and Wind Power Forecasting With Inconsistent Data,” IEEE Transactions on Sustainable Energy, vol. 10, no. 1, pp. 16–25, Jan. 2019, doi: 10.1109/TSTE.2018.2820198. [7] Yun Wang, Qinghua Hu, Deyu Meng, and Pengfei Zhu, “Deterministic and probabilistic wind power forecasting using a variational Bayesian-based adaptive robust multi-kernel regression model,” Applied Energy, vol. 208, pp. 1097–1112, Dec. 2017, doi: 10.1016/j.apenergy.2017.09.043. [6] Haibo Wang, Yun Wang*, and Qinghua Hu, “Self-adaptive robust nonlinear regression for unknown noise via mixture of Gaussians,” Neurocomputing, vol. 235, pp. 274–286, Apr. 2017, doi: 10.1016/j.neucom.2017.01.024. [5] Jian-Zhou Wang and Yun Wang*, “A novel wind speed forecasting model for wind farms of Northwest China,” International Journal of Green Energy, vol. 14, no. 5, pp. 463–478, Apr. 2017, doi: 10.1080/15435075.2016.1278373. [4] Ping Jiang, Yun Wang*, and Jianzhou Wang, “Short-term wind speed forecasting using a hybrid model,” Energy, vol. 119, pp. 561–577, Jan. 2017, doi: 10.1016/j.energy.2016.10.040. [3] Qinghua Hu, Yun Wang*, Zongxia Xie, Pengfei Zhu, and Daren Yu, “On estimating uncertainty of wind energy with mixture of distributions,” Energy, vol. 112, pp. 935–962, Oct. 2016, doi: 10.1016/j.energy.2016.06.112. [2] Yun Wang, Jianzhou Wang, and Xiang Wei, “A hybrid wind speed forecasting model based on phase space reconstruction theory and Markov model: A case study of wind farms in northwest China,” Energy, vol. 91, pp. 556–572, Nov. 2015, doi: 10.1016/j.energy.2015.08.039. [1] Jian-Zhou Wang, Yun Wang*, and Ping Jiang, “The study and application of a novel hybrid forecasting model – A case study of wind speed forecasting in China,” Applied Energy, vol. 143, pp. 472–488, Apr. 2015, doi: 10.1016/j.apenergy.2015.01.038.   教育经历 [1]   2017.9-2018.9 新加坡国立大学  |  电子科学与技术 博士联合培养 [2]   2015.9-2019.6 天津大学  |  计算机应用技术  |  博士学位  |  博士研究生毕业 [3]   2012.9-2015.6 兰州大学  |  应用统计  |  硕士学位  |  硕士研究生毕业 [4]   2008.9-2012.6 安徽大学  |  经济学  |  学士学位  |  大学本科毕业 工作经历 [1]   2019.7-至今 中南大学  |  自动化学院