Unlocking the Potential of Wind Energy With Machine Learning-Based Avian Detection: A Call to Action

Principato, M.; Hasselwander, L.; Stangner, M.; Buettner, R. (2023). Unlocking the Potential of Wind Energy With Machine Learning-Based Avian Detection: A Call to Action. IEEE Access, 11, 23.https://doi.org/10.1109/ACCESS.2023.3287861

@article{Principato-2023-2077798,
author = {Principato, M and Hasselwander, L and Stangner, M and Buettner, R},
title = {Unlocking the Potential of Wind Energy With Machine Learning-Based Avian Detection: A Call to Action},
journal = {IEEE Access},
year = {2023},
month = {jun},
publisher = {IEEE},
volume = {11},
pages = {23},
doi = {10.1109/ACCESS.2023.3287861},
url = {https://ieeexplore.ieee.org/abstract/document/10156843},
keywords = {Wind Energy, Land-Based Wind, Collision, Birds},
}

TY - JOUR
TI - Unlocking the Potential of Wind Energy With Machine Learning-Based Avian Detection: A Call to Action
AU - Principato, M
AU - Hasselwander, L
AU - Stangner, M
AU - Buettner, R
T2 - IEEE Access
AB - This systematic literature review explores the potential of machine learning-based approaches to detect and prevent bird collisions with wind turbines. It provides a comprehensive review of the current approaches and identifies critical gaps in the literature, which may serve as the groundwork for future research and development in this area. As a result, this work highlights the importance of inter- and transdisciplinary cooperations.
DA - 2023/06//
PY - 2023
PB - IEEE
VL - 11
SP - 23
UR - https://ieeexplore.ieee.org/abstract/document/10156843
DO - 10.1109/ACCESS.2023.3287861
LA - English
KW - Wind Energy
KW - Land-Based Wind
KW - Collision
KW - Birds
ER -