A feature engineering approach to wind power forecasting

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.

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Year of publication:	2014
Authors:	Silva, Lucas
Published in:	International Journal of Forecasting. - Elsevier, ISSN 0169-2070. - Vol. 30.2014, 2, p. 395-401
Publisher:	Elsevier
Subject:	GEFCom \| Feature engineering \| Gradient boosted decision trees \| Linear regression \| Machine learning \| Time series

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Type of publication:	Article
Source:	RePEc - Research Papers in Economics

Persistent link: https://www.econbiz.de/10010753464