Wind Power Prediction Using Ensemble Learning-Based Models

• Published in: IEEE access Vol. 8; pp. 61517 - 61527
• Main Authors: Lee, Junho, Wang, Wu, Harrou, Fouzi, Sun, Ying
• Format: Journal Article
• Language: English
• Published: Piscataway IEEE 2020; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Alternative energy sources; Autoregressive processes; Biological system modeling; Boosting; Ensemble learning; Gaussian process; Model accuracy; Performance prediction; Power measurement; Power prediction; Prediction algorithms; Predictive models; regression models; Regression tree analysis; Renewable energy sources; Smart grid; Support vector machines; Time dependence; Wind power; Wind power generation; Wind turbines
• ISSN: 2169-3536; 2169-3536
• DOI: 10.1109/ACCESS.2020.2983234

Wind power is one of the most potential energies and the major available renewable energy sources. Precisely predicting wind power production is essential for the management and the integration of wind power in a smart grid. The goal of this study is to predict wind power production with sufficient accuracy based on various factors using ensemble learning-based methods that consider the time-dependent nature of the wind power measurements. Essentially, the ensemble learning methods combine multiple learners to obtain an enhanced prediction performance in comparison to conventional standalone learners. In addition, they reduce the overall prediction error and have the capacity to merge various models. At first, this paper investigates the prediction capability of the well-known ensemble approaches Boosted Trees, Random Forest, and Generalized Random Forest for wind power prediction. We compared the prediction performance of these ensemble models to two frequently used prediction methods: Gaussian process regression, and Support Vector Regression. Experimental measurements recorded every ten minutes from actual wind turbines located in France and Turkey are used to test the prediction efficiency of the studied models. Experimental results have shown that the ensemble methods can predict wind power production with high accuracy compared to the standalone models. Furthermore, the findings clearly reveal that the lagged variables contribute significantly to the ensemble models, and permits constructing more parsimonious models.