Comparison of recurrent neural networks for wind power forecasting

Erick López, Carlos Valle, Héctor Allende-Cid, Héctor Allende

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

4 Scopus citations


Integrating wind power to the electrical grid is complicated due to the stochastic nature of the wind, which makes its prediction a challenging task. Then, it is important to devise forecasting tools to support this task. For example, a network that integrates an Echo State Network architecture and Long Short-Term Memory blocks as hidden units (ESN+LSTM) has been proposed, showing good performance against a physical model. This paper proposes to compare this network versus Echo State Network (ESN) and Long Short-Term Memory (LSTM), to forecast wind power from 1 to 24 h ahead. Results show than the ESN+LSTM model outperforms the performance reached for ESN and LSTM, in terms of MSE, MAE, and the metrics used in the Taylor diagram. In addition, we observe that the advantage of this network is statistically significant during the first moments of the forecast horizon, in terms of T-test and Wilcoxon-test.

Original languageEnglish
Title of host publicationPattern Recognition - 12th Mexican Conference, MCPR 2020, Proceedings
EditorsKarina Mariela Figueroa Mora, Juan Anzurez Marín, Jaime Cerda, Jesús Ariel Carrasco-Ochoa, José Francisco Martínez-Trinidad, José Arturo Olvera-López
Number of pages10
ISBN (Print)9783030490751
StatePublished - 2020
Event12th Mexican Conference on Pattern Recognition, MCPR 2020 - Morelia, Mexico
Duration: 24 Jun 202027 Jun 2020

Publication series

NameLecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
Volume12088 LNCS
ISSN (Print)0302-9743
ISSN (Electronic)1611-3349


Conference12th Mexican Conference on Pattern Recognition, MCPR 2020


  • Echo State Network
  • Long Short-Term Memory
  • Multivariate time series
  • Recurrent Neural Networks
  • Wind power forecasting


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