Abstract
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.
Highlights
Wind power is clean and renewable energy that gained increased importance and has been incorporated into the available power system
The objective of this paper is to study the prediction performance of the ensemble learning models in enhancing power prediction based on other meteorological factors
We see that the dynamic model (DM) shows the improved prediction performance from the reduced model (RM) with ensemble learning models (RF, generalized random forest (GRF), and XGB)
Summary
Wind power is clean and renewable energy that gained increased importance and has been incorporated into the available power system. In [18], an approach based on a support vector machine scheme is proposed to predict wind power This approach applied data mining to investigate the dependence between wind speed and wind power output and modify the invalid original measurements. It results in acceptable prediction, this approach has not been verified for long-term prediction [18]. The objective of this paper is to study the prediction performance of the ensemble learning models in enhancing power prediction based on other meteorological factors.
Talk to us
Join us for a 30 min session where you can share your feedback and ask us any queries you have