Abstract
Symbolic regression corresponds to an ensemble of techniques that allow to uncover an analytical equation from data. Through a closed form formula, these techniques provide great advantages such as potential scientific discovery of new laws, as well as explainability, feature engineering as well as fast inference. Similarly, deep learning based techniques has shown an extraordinary ability of modeling complex patterns. The present paper aims at applying a recent end-to-end symbolic regression technique, i.e. the equation learner (EQL), to get an analytical equation for wind speed forecasting. We show that it is possible to derive an analytical equation that can achieve reasonable accuracy for short term horizons predictions only using few number of features.
Original language | English |
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Title of host publication | 2021 IEEE Symposium Series on Computational Intelligence (SSCI) |
Publisher | IEEE |
Pages | 01-08 |
Number of pages | 8 |
ISBN (Electronic) | 9781728190488 |
DOIs | |
Publication status | Published - 2021 |
Event | 2021 IEEE Symposium Series on Computational Intelligence - Online, IEEE, Orlando, United States Duration: 5 Dec 2021 → 7 Dec 2021 https://attend.ieee.org/ssci-2021/ |
Symposium
Symposium | 2021 IEEE Symposium Series on Computational Intelligence |
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Abbreviated title | IEEE SSCI 2021 |
Country/Territory | United States |
City | Orlando |
Period | 5/12/21 → 7/12/21 |
Internet address |