On Heterogeneous Machine Learning Ensembles for Wind Power Prediction

For a sustainable integration of wind power into the electricity grid, a precise prediction method is required. In this work, we investigate the use of heterogeneous machine learning ensembles for wind power prediction. We first analyze homogeneous ensemble regressors that make use of a single base algorithm and compare decision trees to k-nearest neighbors and support vector regression. As next step, we construct heterogeneous ensembles that make use of multiple base algorithms and benefit from a gain of diversity of the weak predictors. In the experimental evaluation, we show that a combination of decision trees and support vector regression outperforms state-of-the-art predictors (improvements of up to 37% compared to support vector regression) as well as homogeneous ensembles while requiring a shorter runtime (speed-ups from 1.60x to 8.78x). The experiments are based on large wind time series data from simulations and real measurements.