Machine learning ensembles for wind power prediction

Justin Heinermann and Oliver Kramer

Renewable Energy, 2016, vol. 89, issue C, 671-679

Abstract: 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 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 among 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 1.60× to 8.78×). Furthermore, we show the heterogeneous ensemble prediction can be improved when using high-dimensional patterns by increasing the number of past steps considered and hereby the spatio-temporal information available by the measurements of the nearby turbines. The experiments are based on a large wind time series data set from simulations and real measurements.

Keywords: Wind power prediction; Machine learning ensembles; Multi-inducer; Heterogeneous ensembles; Decision trees; Support vector regression (search for similar items in EconPapers)
Date: 2016
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Citations: View citations in EconPapers (41)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:renene:v:89:y:2016:i:c:p:671-679

DOI: 10.1016/j.renene.2015.11.073

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