LSTM-based multi-scale model for wind speed forecasting

IGNACIO DANIEL ARAYA ZAMORANO, Carlos Valle, Héctor Allende

Resultado de la investigación: Capítulo del libro/informe/acta de congresoContribución a la conferenciarevisión exhaustiva

3 Citas (Scopus)

Resumen

Wind speed forecasting is crucial for the penetration of wind energy sources in electrical systems, since accurate wind speed forecasts directly translates into accurate wind power predictions. A framework called Multi-scale RNNs specifically addresses the issue of learning long term dependencies in RNNs. Following that approach, we devised a LSTM-based Multi-scale model that learns to build different temporal scales from the original wind speed series that are then used as input for multiple LSTMs, whose final internal states are used to forecast wind speed future values. Results from two real wind speed datasets from northern Chile show that this approach outperforms the standard LSTM and its capable of working with very long input series without overfitting, while being computationally efficient regarding training times.

Idioma originalInglés
Título de la publicación alojadaProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 23rd Iberoamerican Congress, CIARP 2018, Proceedings
EditoresRuben Vera-Rodriguez, Julian Fierrez, Aythami Morales
EditorialSpringer Verlag
Páginas38-45
Número de páginas8
ISBN (versión impresa)9783030134686
DOI
EstadoPublicada - 1 ene. 2019
Evento23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018 - Madrid, Espana
Duración: 19 nov. 201822 nov. 2018

Serie de la publicación

NombreLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volumen11401 LNCS
ISSN (versión impresa)0302-9743
ISSN (versión digital)1611-3349

Conferencia

Conferencia23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018
País/TerritorioEspana
CiudadMadrid
Período19/11/1822/11/18

Huella

Profundice en los temas de investigación de 'LSTM-based multi-scale model for wind speed forecasting'. En conjunto forman una huella única.

Citar esto