| 1 |
Wind speed forecasting based on variational mode decomposition and improved echo state network
Hu HL, Wang L, Tao R
Renewable Energy, 164, 729, 2021 |
| 2 |
Experimental Comparison of Solid-state NMR Spectra for Quadrupolar Nuclei Using Various Spin-echo Sequences
Hashi K, Mogami Y, Deguchi K, Ohki S, Goto A, Shimizu T
Chemistry Letters, 49(1), 68, 2020 |
| 3 |
Degradation prediction of PEM fuel cell based on artificial intelligence
Vichard L, Harel F, Ravey A, Venet P, Hissel D
International Journal of Hydrogen Energy, 45(29), 14953, 2020 |
| 4 |
Forecasting energy consumption and wind power generation using deep echo state network
Hu HL, Wang L, Lv SX
Renewable Energy, 154, 598, 2020 |
| 5 |
A Distributed Algorithm for the Cooperative Prediction of Power Production in PV Plants
Rosato A, Panella M, Araneo R
IEEE Transactions on Energy Conversion, 34(1), 497, 2019 |
| 6 |
Speed of sound and derived thermodynamic properties of para-xylene at temperatures between (306 and 448) K and at pressures up to 66 MPa
Al Ghafri SZS, Matabishi EA, Trusler JPM, May EF, Stanwix PL
Journal of Chemical Thermodynamics, 135, 369, 2019 |
| 7 |
Wind speed and wind direction forecasting using echo state network with nonlinear functions
Chitsazan MA, Fadali MS, Trzynadlowski AM
Renewable Energy, 131, 879, 2019 |
| 8 |
Estimate and characterize PV power at demand-side hybrid system
Li Q, Wu Z, Xia XH
Applied Energy, 218, 66, 2018 |
| 9 |
Effective sparse adaboost method with ESN and FOA for industrial electricity consumption forecasting in China
Wang L, Lv SX, Zeng YR
Energy, 155, 1013, 2018 |
| 10 |
Effective electricity energy consumption forecasting using echo state network improved by differential evolution algorithm
Wang L, Hu HL, Ai XY, Liu H
Energy, 153, 801, 2018 |
