Abstract
Snehal Sahu (22129016)
M.Tech, SEE department
OFFSHORE WIND SPEED PREDICTION USING DEEP LEARNING MODELS
Thesis Supervisor: Pradip Swarnakar
April 2025
Accurate wind speed prediction stands at the forefront of renewable energy management,
essential for maintaining the structural integrity of wind turbines and ensuring the seamless
integration of wind power into the electrical grid. This thesis presents an extensive analysis of
various sophisticated machine learning models ranging from LSTM, BiLSTM, and Stacked
LSTM to ConvLSTM, CNN LSTM, GRU, and CNN each tested for their ability to forecast
wind speeds across a diverse set of hub heights, from the lower altitude of 80 meters up to the
lofty height of 200 meters. Initial stationarity verification through the Augmented Dickey-Fuller
(ADF) test forms the basis for a series of experiments designed to pinpoint the most accurate
model in terms of predictive power. The investigation reveals nuanced differences in model
performance, with the Conv LSTM model demonstrating notable accuracy at intermediate hub
heights and the BiLSTM showing its strengths at greater elevations, suggesting a complex
interplay between model architecture and atmospheric dynamics. Meanwhile, the Stacked
LSTM, CNN LSTM, and GRU models each contribute unique insights into the temporal and
spatial aspects of wind speed data. The importance of these findings extends beyond theoretical
analysis; they are critical for the proactive monitoring of turbine health, which directly impacts
their operational longevity and efficiency. Additionally, accurate forecasts are instrumental in
managing the grid's load balance, where the stability of energy supply hinges on the
predictability of wind power input. Looking to the future, this research opens the door to
integrating real-time data feeds and refining machine learning algorithms to not only enhance
the precision of wind speed forecasts but also to fortify the role of wind energy as a reliable and
integral component of global energy systems.
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