Deep Learning-Based Wind Speed Prediction for Optimized Wind Turbine Operation
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Date
2024-05-03
Journal Title
Journal ISSN
Volume Title
Publisher
Nazarbayev University School of Engineering and Digital Sciences
Abstract
Wind energy has been a promising source of clean energy that does not negatively
affect our environment. Because of the fluctuations in wind speed, it is crucial to
predict its values for wind turbines to have the maximum effective power output.
This project aims to develop a way for short-term wind speed prediction based
on deep learning technologies, such as CNN, LSTM, RNN, and GRU models,
alone and in combination. Through iterative experimentation and evaluation, we
develop ten final models and assess their performance based on Mean Squared
Error (MSE), score, and computational efficiency. Our findings reveal that the
GRU model achieves the highest performance with a MSE of 0.00238 m/s and R2
score of 0.8796. Additionaly, the similarly structured LSTM model demonstrates
superior computational efficiency along with high R2 value, outperforming GRU
model. By examining the performance of multiple deep learning architectures,
the project seeks to identify the most suitable approach for wind speed prediction,
thereby facilitating more efficient and sustainable utilization of wind resources
for power generation.
Description
Keywords
Deep learning, wind speed prediction, machine learning, CNN, LSTM, RNN, GRU
Citation
Alimukhambetova, S., & Kochkarova, A. (2024) Deep Learning-Based Wind Speed Prediction for Optimized Wind Turbine Operation. Nazarbayev University School of Engineering and Digital Sciences