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A gradient boosting approach to the Kaggle load forecasting competition

Souhaib Ben Taieb and Rob Hyndman ()

International Journal of Forecasting, 2014, vol. 30, issue 2, 382-394

Abstract: We describe and analyse the approach used by Team TinTin (Souhaib Ben Taieb and Rob J Hyndman) in the Load Forecasting track of the Kaggle Global Energy Forecasting Competition 2012. The competition involved a hierarchical load forecasting problem for a US utility with 20 geographical zones. The data available consisted of the hourly loads for the 20 zones and hourly temperatures from 11 weather stations, for four and a half years. For each zone, the hourly electricity loads for nine different weeks needed to be predicted without having the locations of either the zones or stations. We used separate models for each hourly period, with component-wise gradient boosting for estimating each model using univariate penalised regression splines as base learners. The models allow for the electricity demand changing with the time-of-year, day-of-week, time-of-day, and on public holidays, with the main predictors being current and past temperatures, and past demand. Team TinTin ranked fifth out of 105 participating teams.

Keywords: Short-term load forecasting; Multi-step forecasting; Additive models; Gradient boosting; Machine learning; Kaggle competition (search for similar items in EconPapers)
Date: 2014
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