TY - GEN
T1 - LSTM-based multi-scale model for wind speed forecasting
AU - Araya, Ignacio A.
AU - Valle, Carlos
AU - Allende, Héctor
N1 - Publisher Copyright:
© Springer Nature Switzerland AG 2019.
PY - 2019
Y1 - 2019
N2 - 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.
AB - 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.
KW - Long Short-Term Memory
KW - Multi-scale recurrent networks
KW - Wind speed forecasting
UR - http://www.scopus.com/inward/record.url?scp=85063044057&partnerID=8YFLogxK
U2 - 10.1007/978-3-030-13469-3_5
DO - 10.1007/978-3-030-13469-3_5
M3 - Conference contribution
AN - SCOPUS:85063044057
SN - 9783030134686
T3 - Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics)
SP - 38
EP - 45
BT - Progress in Pattern Recognition, Image Analysis, Computer Vision, and Applications - 23rd Iberoamerican Congress, CIARP 2018, Proceedings
A2 - Vera-Rodriguez, Ruben
A2 - Fierrez, Julian
A2 - Morales, Aythami
PB - Springer Verlag
T2 - 23rd Iberoamerican Congress on Pattern Recognition, CIARP 2018
Y2 - 19 November 2018 through 22 November 2018
ER -