(1)第一且通讯作者. Short-term power load forecasting under unstable data quality: a fuzzy deep neural network with LSTM and self-attention [J]. IEEE Transactions on Industrial Informatics, 2025.(SCI一区TOP) (2)通讯作者. Short-term probabilistic load forecasting using quantile regression neural network with accumulated hidden layer connection structure [J]. IEEE Transactions on Industrial Informatics, 2023.(SCI一区TOP) (3)第一且通讯作者. A parallel short-term power load forecasting method considering high-level elastic loads [J]. IEEE Transactions on Instrumentation and Measurement, 2023, 72:1-10.(SCI二区TOP) (4)第一且通讯作者. Transmission expansion planning: A deep learning approach [J]. Sustainable Energy, Grids and Networks, 2025, 41: 101585.(SCI二区) (5)通讯作者. A distributed short-term load forecasting method in consideration of holiday distinction [J]. Sustainable Energy, Grids and Networks, 2024: 101296.(SCI二区) (6)第一且通讯作者. Short-term power load forecasting using bidirectional gated recurrent units-based adaptive stacked autoencoder [J]. International Journal of Electrical Power and Energy Systems, 2025, 165: 110459.(SCI二区) (7)第一且通讯作者. An intraperiod arbitrary ram-rate changing model in unit commitment [J]. Energy, 2023, 284: 128593.(SCI一区TOP) (8)第一作者. Day-ahead wind-thermal unit commitment considering historical virtual wind power data [J]. Energy, 2021, 235: 121324.(SCI一区TOP) (9)通讯作者. A more accurate piecewise linear approximation method for quadratic cost curves of thermal generators and its application in unit commitment [J]. Electrical Engineering, 2024: 1-12.(SCI四区) (10)通讯作者. Selection of desirable transmission power mode for the bundled wind-thermal generation systems[J]. Journal of Cleaner Production, 2019, 216: 585-596.(SCI一区TOP) (11)通讯作者. Optimal day-ahead wind-thermal unit commitment considering statistical and predicted features of wind speeds [J]. Energy Conversion and Management, 2017, 142: 347-356.(SCI一区TOP) (12)第一作者. Accuracy study of linearization methods for quadratic cost curves of thermal units in unit commitment problems [J]. IET Generation, Transmission & Distribution, 2022, 1-10.(SCI四区) (13)导师一作,本人二作. Optimal capacity and type planning of generating units in a bundled wind-thermal generation system [J]. Applied Energy, 2016, 164: 200-210.(SCI一区TOP) (14)导师一作,本人二作. Optimal planning of HVDC-based bundled wind-thermal generation and transmission system [J]. Energy Conversion and Management, 2016, 115: 71-79.(SCI一区TOP) (15)第一作者. Optimal planning of bundled wind-thermal generation systems: A case study of China [J]. Journal of Renewable and Sustainable Energy, 2016, 8(1): 13308.(SCI四区) (16)第一作者. 计及风速和负荷特性的风火打捆发电系统规划[J]. 电力系统自动化, 2016, 40(15): 59-66.(EI) (17)第一作者. Reliability evaluation of multi-terminals VSC-HVDC system[J]. Journal of Electrical Systems, 2015,11(3):342-352.(EI) (18)第一完成人. 一种基于深度学习和数据驱动的电力负荷预测方法,CN120016472B.(发明专利) (19)第一完成人. 一种基于树模型的新能源汽车充电电量预测方法,CN120031258B.(发明专利) (20)第一完成人. 一种基于小样本数据的电量预测方法,CN120031264B.(发明专利) (21)第一完成人. 一种特征融合图神经网络的短期住宅用电量预测方法,CN119476652B.(发明专利) (22)第一完成人. 一种基于Prophet的中期电量预测方法,CN119965870B.(发明专利) (23)第一完成人. 一种考虑置信区间的深度学习机组组合问题求解方法,CN118735309B.(发明专利) (24)第一完成人. 一种考虑机组置信度的深度学习机组组合问题求解方法,CN118246351B.(发明专利) |