Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Title A name given to the resource Faculty Publications Article Faculty Publications -Articles Dublin Core The Dublin Core metadata element set is common to all Omeka records, including items, files, and collections. For more information see, http://dublincore.org/documents/dces/. Creator An entity primarily responsible for making the resource Muthamizhan, T.; Karthick, K.; Aruna, S.K.; Velmurugan, P. Title A name given to the resource AI-Driven Stacking Ensemble for Predicting Total Power Output of Wave Energy Converters: A Data-Driven Approach to Renewable Energy Processes Date A point or period of time associated with an event in the lifecycle of the resource 01-01-2025 Source A related resource from which the described resource is derived Processes;Volume;13;Issue;4;Article No.;961; Identifier An unambiguous reference to the resource within a given context <a href="https://doi.org/10.3390/pr13040961" target="_blank" rel="noreferrer noopener">https://doi.org/10.3390/pr13040961</a> <br /><br /><a href="https://www.scopus.com/pages/publications/105003713611?origin=resultslist" target="_blank" rel="noreferrer noopener">https://www.scopus.com/pages/publications/105003713611?origin=resultslist</a> Coverage The spatial or temporal topic of the resource, the spatial applicability of the resource, or the jurisdiction under which the resource is relevant Muthamizhan T., Department of Electrical and Electronics Engineering, Sri Sai Ram Institute of Technology, Tamil Nadu, Chennai, 600044, India; Karthick K., Department of Electrical and Electronics Engineering, GMR Institute of Technology, Rajam Andhra Pradesh, 532127, India; Aruna S.K., Department of Computer Science and Engineering, School of Engineering and Technology, CHRIST University, Karnataka, Bangalore, 560074, India; Velmurugan P., Department of Electrical and Electronics Engineering, St. Josephs College of Engineering, Tamil Nadu, Chennai, 600119, India Description An account of the resource This study develops and evaluates an AI-driven stacked hybrid machine learning model for predicting the total power output of wave energy converters (WECs) across four Australian coastal locations: Adelaide, Perth, Sydney, and Tasmania. This research enhances prediction accuracy through advanced ensemble learning techniques while addressing spatial variability in wave energy processes. The dataset comprises spatial coordinates and power output readings from 16 fully submerged WECs per location, capturing the variability of wave energy across different coastal regions. Data preprocessing included missing value imputation, duplicate removal, and spatial feature transformation via Euclidean distance calculation. Principal component analysis (PCA) was employed to reduce dimensionality while preserving critical features influencing power generation. To develop an accurate prediction model, we employed a stacking ensemble approach using XGBoost, LightGBM, and CatBoost as base learners, optimized via Optuna hyperparameter tuning with 10-fold cross-validation. A Ridge regression meta-learner combined the outputs of these models, leveraging their complementary strengths to enhance predictive performance. Experimental results demonstrate that the hybrid model consistently outperforms individual models, enhancing predictive accuracy across all locations. Sydney exhibited the highest accuracy (RMSE = 9089.58 W, R2 = 0.8576), while Tasmania posed the greatest challenge (RMSE = 45,032.37 W, R2 = 0.8378). The ensemble approach mitigated overfitting and improved generalization by leveraging the complementary strengths of XGBoost, LightGBM, and CatBoost. By leveraging AI-driven ensemble learning, this study provides a scalable and reliable framework for wave energy forecasting, facilitating more efficient grid integration and resource planning in renewable energy systems. 2025 by the authors. Subject The topic of the resource AI-driven energy forecasting; machine learning; renewable energy; sustainable energy systems; wave energy converters Publisher An entity responsible for making the resource available Multidisciplinary Digital Publishing Institute (MDPI) Relation A related resource ISSN: 22279717; Language A language of the resource English Type The nature or genre of the resource Article Rights Information about rights held in and over the resource All Open Access; Gold Open Access Format The file format, physical medium, or dimensions of the resource online