Advancing Offshore Wind Turbine Energy Generation Prediction Through Comparative Analysis and Novel Machine Learning Techniques

The necessity for this work arises from the critical need to accurately predict energy generation from offshore wind turbines, pivotal in ensuring efficient energy management and grid integration process. While existing studies have made strides, they are often limited by their reliance on simplistic models or a lack of comprehensive comparative analyses. To address these limitations, this paper proposes a novel approach integrating advanced machine learning (ML) algorithms with state-of-the-art techniques. The proposed model leverages a comprehensive comparative analysis of ML algorithms, including Linear Regression, Random Forest, Gradient Boosting, Support Vector Machines (SVM), and Neural Networks (NN), to identify the most effective predictive model. By considering parameters such as tower height and blade length, the model achieves superior accuracy and robustness in energy generation prediction. Furthermore, this study introduces novel methodologies, including ensemble learning, transfer learning, explainable AI (XAI), ML techniques to enhance predictive performance and interpretability sets. By incorporating advanced time-series analysis and Bayesian optimization, the model captures temporal dependencies and efficiently optimizes hyperparameters, respectively, elevating its predictive capabilities. The impact of this work extends beyond academia, with implications for industry stakeholders involved in offshore wind energy production. Accurate energy generation predictions empower decision-makers to optimize turbine design and placement, ultimately advancing the transition towards sustainable energy solutions. Moreover, the integration of XAI techniques fosters trust and usability in real-world applications, bridging the gap between predictive accuracy and interpretability sets.