| 1 |
Very short-term wind power density forecasting through artificial neural networks for microgrid control
Rodriguez F, Florez-Tapia AM, Fontan L, Galarza A
Renewable Energy, 145, 1517, 2020 |
| 2 |
On wind speed pattern and energy potential in China
Liu F, Sun FB, Liu WW, Wang TT, Wang H, Wang XM, Lim WH
Applied Energy, 236, 867, 2019 |
| 3 |
An investigation of wind power density distribution at location with low an high wind speeds using statistical model
Katinas V, Gecevicius G, Marciukaitis M
Applied Energy, 218, 442, 2018 |
| 4 |
Assessment of onshore wind energy potential under different geographical climate conditions in China
Li Y, Wu XP, Li QS, Tee KF
Energy, 152, 498, 2018 |
| 5 |
Discerning the spatial variations in offshore wind resources along the coast of China via dynamic downscaling
Liu YC, Chen DY, Li SW, Chan PW
Energy, 160, 582, 2018 |
| 6 |
Extrapolating wind data at high altitudes with high precision methods for accurate evaluation of wind power density, case study: Center of Iran
Faghani GR, Ashrafi ZN, Sedaghat A
Energy Conversion and Management, 157, 317, 2018 |
| 7 |
Evaluation of hydrogen production from harvesting wind energy at high altitudes in Iran by three extrapolating Weibull methods
Ashrafi ZN, Ghasemian M, Shahrestani MI, Khodabandeh E, Sedaghat A
International Journal of Hydrogen Energy, 43(6), 3110, 2018 |
| 8 |
New methods to assess wind resources in terms of wind speed, load, power and direction
Gugliani GK, Sarkar A, Ley C, Mandal S
Renewable Energy, 129, 168, 2018 |
| 9 |
Spatial distribution of offshore wind statistics on the coast of Portugal using Regional Frequency Analysis
Campos RM, Soares CG
Renewable Energy, 123, 806, 2018 |
| 10 |
Preliminary assessment of the wind power resource around the thousand-meter scale megatall building
Cao JL, Man XX, Liu J, Liu L, Shui TT
Energy and Buildings, 142, 62, 2017 |
