Comparison of Models for Wind Speed Prediction Through Neural Networks in Lençóis, Bahia

Amanda Santana Lins Bispo; Lian Filipe Santana Nascimento; Yuri de Jesus Gomes; Flávio Santos Conterato

doi:10.34178/jbth.v5i2.209

Amanda Santana Lins Bispo
Lian Filipe Santana Nascimento
Yuri de Jesus Gomes
Flávio Santos Conterato

DOI: https://doi.org/10.34178/jbth.v5i2.209

Keywords: Artificial Intelligence, Machine Learning, Neural Networks, Wind Speed, Wind Speed Forecasting

Abstract

This study aims to compare forecasting models using artificial intelligence to conclude which is the best one for the forecasting wind speed for 1 hour in Lençóis, BA, using a data source from the Instituto Nacional de Meteorologia (INMET). Furthermore, an Artificial Neural Network (ANN) was developed using TensorFlow and Keras libraries, it was compared with other forecasting models, which showed to be the most efficient among the options for this purpose. Moreover, the principal metric used to evaluate this study was Mean Absolute Error (MAE), and the auxiliary ones were Mean Squared Error (MSE) and R². The RNA obtained the following values for each metric: 0.421 for MAE, 0.389 for MSE, and 0.523 for the R² metric.