EventsThe 5th International Electronic Conference on Atmospheric Sciences
Published
This submission belongs to the session S4. Atmospheric Techniques, Instruments, and Modeling of the event The 5th International Electronic Conference on Atmospheric Sciences
Published date
25 Jul, 2022
Academic Editor
author-avatarAnthony Lupo
Citation
Maibys Sierra Lorenzo, Adrián Fuentes Barrios, Alfredo E. Roque Rodríguez, LSTM model for wind speed and power generation nowcasting., in Proceedings of The 5th International Electronic Conference on Atmospheric Sciences, 16 July–31 July 2022, MDPI: Basel, Switzerland, doi: 10.3390/ecas2022-12851
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LSTM model for wind speed and power generation nowcasting.

Alfredo E. Roque Rodríguez 1
1. Center for Atmospheric Physics, Meteorological Institute of Cuba
2. Center for Atmospheric Physics, Meteorological Institute of Cuba, Cuba
Abstract

In the following work, the design of an LSTM-type neural network model for wind speed and power generation nowcasting, every 10 minutes and up to two hours, is presented. For this, the wind speed measurements were used every 10 minutes at different heights above the ground, coming from the Measurement Tower located in Los Cocos, in the province of Holguín (Cuba), where the wind farms Gibara I and II are located. The real data is complemented with the wind speed numerical hourly forecasts from SisPI. The data covered the period between February 1, 2019 and January 31, 2020, that is, one year of measurement. Several LSTM models were built and evaluated considering only the measurements and combining the measurements with the forecasts generated by SisPI. The results suggest that the constructed models perform better than other more traditional statistical models and than other neural network models used in the country for similar purposes.

Keywords
wind speed and power generation nowcasting
renewable energy sources
artificial neural networks
SisPI
Manuscript
DEVELOPMENT AND PATH OF HURRICANE ETA. CASE STUDY USING THE WRF MODEL WITH DYNAMIC UPDATING OF THE SST.
Comparing methods to estimate cloud at the Geophysical Observatory of the Institute of Solar-Terrestrial Physics SB RAS (Tory, Republic of Buryatia, Russia) in December 2020