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[1]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]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]Tuo Chen, Runmin Zou, Fan Zhang, Lingjun Zhang. Ensemble probabilistic wind power forecasting with multi-scale features Renewable Energy, 2022, 201: 734–751
[4]Houhua Xu, Lingjun Zhang. A deep asymmetric Laplace neural network for deterministic and probabilistic wind power forecasting Renewable Energy, 2022, 196: 497–517
[5]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]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]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]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]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]Runmin Zou, Dongran Song. Bayesian infinite mixture models for wind speed distribution estimation Energy Conversion and Management, 2021, 236: 113946
[11]Zhiya Chen, Runmin Zou. Bayesian robust multi-extreme learning machine Knowledge-Based Systems, 2020, 210: 106468
[12]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]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]Shenglei Pei. Wind Power Curve Modeling with Asymmetric Error Distribution IEEE Transactions on Sustainable Energy, 2019,
[15]Dipti Srinivasan, Qinghua Hu. Robust functional regression for wind speed forecasting based on Sparse Bayesian learning Renewable Energy, 2019, 132: 43-60

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