A feature engineering approach to wind power forecasting

Lucas Silva

International Journal of Forecasting, 2014, vol. 30, issue 2, 395-401

Abstract: This paper provides detailed information about team Leustagos’ approach to the wind power forecasting track of GEFCom 2012. The task was to predict the hourly power generation at seven wind farms, 48 hours ahead. The problem was addressed by extracting time- and weather-related features, which were used to build gradient-boosted decision trees and linear regression models. This approach achieved first place in both the public and private leaderboards.

Keywords: GEFCom; Feature engineering; Gradient boosted decision trees; Linear regression; Machine learning; Time series (search for similar items in EconPapers)
Date: 2014
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Citations: View citations in EconPapers (9)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:intfor:v:30:y:2014:i:2:p:395-401

DOI: 10.1016/j.ijforecast.2013.07.007

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