| Modeling wind-turbine power curve: A data partitioning and mining approach T Ouyang, A Kusiak, Y He Renewable Energy 102, 1-8, 2017 | 208 | 2017 |
| Modeling and forecasting short-term power load with copula model and deep belief network T Ouyang, Y He, H Li, Z Sun, S Baek IEEE Transactions on Emerging Topics in Computational Intelligence 3 (2 …, 2019 | 186 | 2019 |
| A combined multivariate model for wind power prediction T Ouyang, X Zha, L Qin Energy Conversion and Management 144, 361-373, 2017 | 120 | 2017 |
| Auto-encoder-extreme learning machine model for boiler NOx emission concentration prediction Z Tang, S Wang, X Chai, S Cao, T Ouyang, Y Li Energy 256, 124552, 2022 | 110 | 2022 |
| Two-phase Deep Learning Model for Short-term Wind Direction Forecasting Z Tang, G Zhao, T Ouyang Renewable Energy 173, 1005-1016, 2021 | 110 | 2021 |
| Advanced wind power prediction based on data-driven error correction J Yan, T Ouyang Energy conversion and management 180, 302-311, 2019 | 102 | 2019 |
| Chaotic wind power time series prediction via switching data-driven modes T Ouyang, H Huang, Y He, Z Tang Renewable Energy 145, 270-281, 2020 | 85 | 2020 |
| Predictive model of yaw error in a wind turbine T Ouyang, A Kusiak, Y He Energy 123, 119-130, 2017 | 73 | 2017 |
| Prediction of wind power ramp events based on residual correction T Ouyang, X Zha, L Qin, Y He, Z Tang Renewable energy 136, 781-792, 2019 | 69 | 2019 |
| A survey of wind power ramp forecasting T Ouyang, X Zha, L Qin Energy and Power Engineering 5 (04), 368, 2013 | 57 | 2013 |
| Sample-based neural approximation approach for probabilistic constrained programs X Shen, T Ouyang, N Yang, J Zhuang IEEE Transactions on Neural Networks and Learning Systems 34 (2), 1058-1065, 2021 | 53 | 2021 |
| Rule-based modeling with DBSCAN-based information granules T Ouyang, W Pedrycz, NJ Pizzi IEEE Transactions on Cybernetics 51 (7), 3653-3663, 2019 | 45 | 2019 |
| Cooperative comfortable-driving at signalized intersections for connected and automated vehicles X Shen, X Zhang, T Ouyang, Y Li, P Raksincharoensak IEEE Robotics and Automation Letters 5 (4), 6247-6254, 2020 | 44 | 2020 |
| A wind turbine bearing fault diagnosis method based on fused depth features in time–frequency domain Z Tang, M Wang, T Ouyang, F Che Energy Reports 8, 12727-12739, 2022 | 43 | 2022 |
| Granular description of data structures: A two-phase design T Ouyang, W Pedrycz, OF Reyes-Galaviz, NJ Pizzi IEEE transactions on cybernetics 51 (4), 1902-1912, 2019 | 43 | 2019 |
| Mixture density networks-based knock simulator X Shen, T Ouyang, C Khajorntraidet, Y Li, S Li, J Zhuang IEEE/ASME Transactions on Mechatronics 27 (1), 159-168, 2021 | 34 | 2021 |
| A deep learning framework for short-term power load forecasting T Ouyang, Y He, H Li, Z Sun, S Baek arXiv preprint arXiv:1711.11519, 2017 | 34 | 2017 |
| Ramp events forecasting based on long‐term wind power prediction and correction T Ouyang, H Huang, Y He IET Renewable Power Generation 13 (15), 2793-2801, 2019 | 32 | 2019 |
| Monitoring wind turbines' unhealthy status: a data-driven approach T Ouyang, Y He, H Huang IEEE Transactions on Emerging Topics in Computational Intelligence 3 (2 …, 2018 | 31 | 2018 |
| Wind power prediction method based on regime of switching kernel functions T Ouyang, X Zha, L Qin, Y Xiong, T Xia Journal of Wind Engineering and Industrial Aerodynamics 153, 26-33, 2016 | 29 | 2016 |
