Neural Networks for Improving Wind Power Efficiency: A Review
The demand for wind energy harvesting has grown significantly to mitigate the global challenges of climate change, energy security, and zero carbon emissions. Various methods to maximize wind power efficiency have been proposed. Notably, neural networks have shown large potential in improving wind p...
Main Authors: | Heesoo Shin, Mario Rüttgers, Sangseung Lee |
---|---|
Format: | Article |
Language: | English |
Published: |
MDPI AG
2022-11-01
|
Series: | Fluids |
Subjects: | |
Online Access: | https://www.mdpi.com/2311-5521/7/12/367 |
Similar Items
-
Surrogate Models for Wind Turbine Electrical Power and Fatigue Loads in Wind Farm
by: Georgios Gasparis, et al.
Published: (2020-12-01) -
T2FL: An Efficient Model for Wind Turbine Fatigue Damage Prediction for the Two-Turbine Case
by: Christos Galinos, et al.
Published: (2020-03-01) -
Data‐driven stochastic model predictive control for regulating wind farm power generation with controlled battery storage
by: Zishuo Huang, et al.
Published: (2023-10-01) -
Wind power generation and wind turbine design /
by: Tong, Wei
Published: (c201) -
Surrogate Modeling and Aeroelastic Analysis of a Wind Turbine with Down-Regulation, Power Boosting, and IBC Capabilities
by: Vasilis Pettas, et al.
Published: (2024-03-01)