Symbolic regression for scientific discovery: an application to wind speed forecasting
This work addresses wind speed forecasting for renewable energy applications, but it is incremental as it applies an existing method to a specific domain.
The paper tackled wind speed forecasting by applying the equation learner (EQL) symbolic regression technique to derive an analytical equation, achieving reasonable accuracy for short-term predictions with few features.
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.