Version 1
: Received: 25 September 2023 / Approved: 27 September 2023 / Online: 28 September 2023 (11:41:50 CEST)
How to cite:
Alkhayat, G.; Hasan, S.H.; Mehmood, R. Hybrid Model of VMD and LSTM for Next-Hour Wind Speed Forecasting in a Hot Desert Climate. Preprints2023, 2023091966. https://doi.org/10.20944/preprints202309.1966.v1
Alkhayat, G.; Hasan, S.H.; Mehmood, R. Hybrid Model of VMD and LSTM for Next-Hour Wind Speed Forecasting in a Hot Desert Climate. Preprints 2023, 2023091966. https://doi.org/10.20944/preprints202309.1966.v1
Alkhayat, G.; Hasan, S.H.; Mehmood, R. Hybrid Model of VMD and LSTM for Next-Hour Wind Speed Forecasting in a Hot Desert Climate. Preprints2023, 2023091966. https://doi.org/10.20944/preprints202309.1966.v1
APA Style
Alkhayat, G., Hasan, S.H., & Mehmood, R. (2023). Hybrid Model of VMD and LSTM for Next-Hour Wind Speed Forecasting in a Hot Desert Climate. Preprints. https://doi.org/10.20944/preprints202309.1966.v1
Chicago/Turabian Style
Alkhayat, G., Syed Hamid Hasan and Rashid Mehmood. 2023 "Hybrid Model of VMD and LSTM for Next-Hour Wind Speed Forecasting in a Hot Desert Climate" Preprints. https://doi.org/10.20944/preprints202309.1966.v1
Abstract
Advancements in technology, policies, and cost reductions have led to rapid growth in wind power production. One of the major challenges in wind energy production is the instability of wind power generation due to weather changes. Efficient power grid management requires accurate power output forecasting. New wind energy forecasting methods based on deep learning are better than traditional methods, like numerical weather prediction, statistical models, and machine learning models. This is more true for short-term prediction. Since there is a relationship between methods, climates, and forecasting complexity, forecasting methods do not always perform the same depending on the climate and terrain of the data source. This paper proposes a novel model that combines the variational mode decomposition method with a long short-term memory model, developed for next-hour wind speed prediction in a hot desert climate, such as the climate in Saudi Arabia. We compared the proposed model performance to two other hybrid models, six deep learning models, and four machine learning models using different feature sets. Also, we tested the proposed model on data from different climates, Caracas and Toronto. The proposed model showed a forecast skill between 61% to 74% based on mean absolute error, 64% to 72% based on root mean square error, and 59% to 68% based on mean absolute percentage error for locations in Saudi Arabia.
Computer Science and Mathematics, Artificial Intelligence and Machine Learning
Copyright:
This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.
