Automated Machine Learning for Wind Farms Location

Olivier Parisot, Thomas Tamisier

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.

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Paper Citation


in Harvard Style

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 - Volume 1: ICPRAM, ISBN 978-989-758-486-2, pages 222-227. DOI: 10.5220/0010232102220227


in Bibtex Style

@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 - Volume 1: ICPRAM,},
year={2021},
pages={222-227},
publisher={SciTePress},
organization={INSTICC},
doi={10.5220/0010232102220227},
isbn={978-989-758-486-2},
}


in EndNote Style

TY - CONF

JO - Proceedings of the 10th International Conference on Pattern Recognition Applications and Methods - Volume 1: ICPRAM,
TI - Automated Machine Learning for Wind Farms Location
SN - 978-989-758-486-2
AU - Parisot O.
AU - Tamisier T.
PY - 2021
SP - 222
EP - 227
DO - 10.5220/0010232102220227