Symbolic regression for scientific discovery: an application to wind speed forecasting

TL;DR

This paper applies a recent symbolic regression method, EQL, to derive an analytical wind speed forecasting equation, demonstrating its potential for scientific discovery and accurate short-term predictions with few features.

Contribution

It introduces the application of the EQL symbolic regression technique to wind speed forecasting, highlighting its ability to produce interpretable equations with limited features.

Findings

Derived an analytical wind speed forecasting equation

Achieved reasonable short-term prediction accuracy

Used few features for effective modeling

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.