Novel Hybrid Deep Learning Model for Forecasting FOWT Power Output

Mohammad Barooni, Deniz Velioglu Sogut (), Parviz Sedigh and Masoumeh Bahrami
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Mohammad Barooni: Ocean Engineering and Marine Science, Florida Institute of Technology, Melbourne, FL 32901, USA
Deniz Velioglu Sogut: Ocean Engineering and Marine Science, Florida Institute of Technology, Melbourne, FL 32901, USA
Parviz Sedigh: Mechanical Engineering, University of New Hampshire, Durham, NH 03824, USA
Masoumeh Bahrami: Electrical and Computer Engineering, University of New Hampshire, Durham, NH 03824, USA

Energies, 2025, vol. 18, issue 13, 1-17

Abstract: This study presents a novel approach in the field of renewable energy, focusing on the power generation capabilities of floating offshore wind turbines (FOWTs). The study addresses the challenges of designing and assessing the power generation of FOWTs due to their multidisciplinary nature involving aerodynamics, hydrodynamics, structural dynamics, and control systems. A hybrid deep learning model combining Convolutional Neural Networks (CNNs) and Long Short-Term Memory (LSTM) networks is proposed to predict the performance of FOWTs accurately and more efficiently than traditional numerical models. This model addresses computational complexity and lengthy processing times of conventional models, offering adaptability, scalability, and efficient handling of nonlinear dynamics. The results for predicting the generator power of a spar-type floating offshore wind turbine (FOWT) in a multivariable parallel time-series dataset using the Convolutional Neural Network–Long Short-Term Memory (CNN-LSTM) model showed promising outcomes, offering valuable insights into the model’s performance and potential applications. Its ability to capture a comprehensive range of load case scenarios—from mild to severe—through the integration of multiple relevant features significantly enhances the model’s robustness and applicability in realistic offshore environments. The research demonstrates the potential of deep learning methods in advancing renewable energy technology, specifically in optimizing turbine efficiency, anticipating maintenance needs, and integrating wind power into energy grids.

Keywords: deep learning; offshore wind turbine; neural networks; CNN-LSTM (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2025
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