Automated Machine Learning for Wind Farms Location

Olivier Parisot; Thomas Tamisier

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Paper

Automated Machine Learning for Wind Farms Location

Topics: Advanced Learning Methods; Classification and Clustering; Industry Related Applications; Machine Learning Methods

In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods ICPRAM - Volume 1, 222-227, 2021

Authors: Olivier Parisot and Thomas Tamisier

Affiliation: Luxembourg Institute of Science and Technology (LIST), Luxembourg

Keyword(s): Automated Machine Learning, Wind Farms Location.

Abstract: Automated Machine Learning aims at preparing effective Machine Learning models with little or no data science expertise. Tedious tasks like preprocessing, algorithm selection and hyper-parameters optimization are then automatized: end-users just have to apply and deploy the model that best suits the real world problem. In this paper, we experiment Automated Machine Learning to leverage open data sources for predicting potential next wind farms location in Luxembourg, France, Belgium and Germany.

CC BY-NC-ND 4.0

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Paper citation in several formats:

Parisot, O. and Tamisier, T. (2021). Automated Machine Learning for Wind Farms Location. In Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM; ISBN 978-989-758-486-2; ISSN 2184-4313, SciTePress, pages 222-227. DOI: 10.5220/0010232102220227

@conference{icpram21,
author={Olivier Parisot. and Thomas Tamisier.},
title={Automated Machine Learning for Wind Farms Location},
booktitle={Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM},
year={2021},
pages={222-227},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010232102220227},
isbn={978-989-758-486-2},
issn={2184-4313},
}

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - ICPRAM
TI - Automated Machine Learning for Wind Farms Location
SN - 978-989-758-486-2
IS - 2184-4313
AU - Parisot, O.
AU - Tamisier, T.
PY - 2021
SP - 222
EP - 227
DO - 10.5220/0010232102220227
PB - SciTePress