LSTM-based multi-scale model for wind speed forecasting

Ignacio A. Araya, Carlos Valle, Héctor Allende

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

3 Scopus citations


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.

Original languageEnglish
Title of host publicationProgress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 23rd Iberoamerican Congress, CIARP 2018, Proceedings
EditorsRuben Vera-Rodriguez, Julian Fierrez, Aythami Morales
PublisherSpringer Verlag
Number of pages8
ISBN (Print)9783030134686
StatePublished - 2019
Externally publishedYes
Event23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018 - Madrid, Spain
Duration: 19 Nov 201822 Nov 2018

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume11401 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018


  • Long Short-Term Memory
  • Multi-scale recurrent networks
  • Wind speed forecasting


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