| 1 |
Effective wind power prediction using novel deep learning network: Stacked independently recurrent autoencoder
Wang L, Tao R, Hu HL, Zeng YR
Renewable Energy, 164, 642, 2021 |
| 2 |
Ultra-short-term combined prediction approach based on kernel function switch mechanism
Lu P, Ye L, Tang Y, Zhao YN, Zhong WZ, Qu Y, Zhai BX
Renewable Energy, 164, 842, 2021 |
| 3 |
Chaotic wind power time series prediction via switching data-driven modes
Ouyang TH, Huang HM, He YS, Tang ZH
Renewable Energy, 145, 270, 2020 |
| 4 |
Coordinated operation of electric vehicle charging and wind power generation as a virtual power plant: A multi-stage risk constrained approach
Abbasi MH, Taki M, Rajabi A, Li L, Zhang JF
Applied Energy, 239, 1294, 2019 |
| 5 |
Robust short-term prediction of wind power generation under uncertainty via statistical interpretation of multiple forecasting models
Korprasertsak N, Leephakpreeda T
Energy, 180, 387, 2019 |
| 6 |
Advanced wind power prediction based on data-driven error correction
Yan J, Ouyang TH
Energy Conversion and Management, 180, 302, 2019 |
| 7 |
A novel clustering algorithm based on mathematical morphology for wind power generation prediction
Hao Y, Dong L, Liao XZ, Liang J, Wang LJ, Wang B
Renewable Energy, 136, 572, 2019 |
| 8 |
A novel hybrid technique for prediction of electric power generation in wind farms based on WIPSO, neural network and wavelet transform
Esfetang NN, Kazemzadeh R
Energy, 149, 662, 2018 |
| 9 |
Wind power prediction using hybrid autoregressive fractionally integrated moving average and least square support vector machine
Yuan XH, Tan QX, Lei XH, Yuan YB, Wu XT
Energy, 129, 122, 2017 |
| 10 |
A new chaotic time series hybrid prediction method of wind power based on EEMD-SE and full-parameters continued fraction
Wang C, Zhang HL, Fan WH, Ma P
Energy, 138, 977, 2017 |
