Data-driven symbolic ensemble models for wind speed forecasting through evolutionary algorithms

doi:10.1016/j.asoc.2019.105976

Data-driven symbolic ensemble models for wind speed forecasting through evolutionary algorithms

2020-02-01

7

PRE

AI

Pedro Leite da Silva Dias

H

Hélio J. C. Barbosa

DOI：10.1016/j.asoc.2019.105976

Original

Original request for help

Save

Abstract

En 中文

Non-linear data-driven symbolic models have been gaining traction in many fields due to their distinctive combination of modeling expressiveness and interpretability. Despite that, they are still rather unexplored for ensemble wind speed forecasting, leaving behind new promising avenues for advancing the development of more accurate models which impact the efficiency of energy production. In this work, we develop a methodology based on the evolutionary algorithm known as grammatical evolution, and apply it to build forecasting models of near-surface wind speed over five locations in northeastern Brazil. Taking advantage of the symbolic nature of the models built, we conducted an extensive series of post-analyses. Overall, our models reduced the forecasting errors by 7%-56% when compared with other techniques, including a real-world operational ensemble model used in Brazil. (C) 2019 Elsevier B.V. All rights reserved.

Keywords:

Ensemble weather forecast

Wind speed

Northeastern Brazil

Grammatical evolution

Machine learning

Data-driven modeling

Data-driven symbolic ensemble models for wind speed forecasting through evolutionary algorithms

Abstract

AI Summary

Journal

Organization