GEFCOM 2014 - Probabilistic Electricity Price Forecasting

Gergo Barta; Gyula Borbely; Gabor Nagy; Sandor Kazi; Tamas Henk

doi:10.1007/978-3-319-19857-6_7

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arXiv · 1506.06972

GEFCOM 2014 - Probabilistic Electricity Price Forecasting

Gergo Barta·Gyula Borbely·Gabor Nagy·Sandor Kazi·Tamas Henk

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Abstract

Energy price forecasting is a relevant yet hard task in the field of multi-step time series forecasting. In this paper we compare a well-known and established method, ARMA with exogenous variables with a relatively new technique Gradient Boosting Regression. The method was tested on data from Global Energy Forecasting Competition 2014 with a year long rolling window forecast. The results from the experiment reveal that a multi-model approach is significantly better performing in terms of error metrics. Gradient Boosting can deal with seasonality and auto-correlation out-of-the box and achieve lower rate of normalized mean absolute error on real-world data.

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Gergo Barta, Gyula Borbely, Gabor Nagy, Sandor Kazi, Tamas Henk. 2015-06-23. GEFCOM 2014 - Probabilistic Electricity Price Forecasting. https://doi.org/10.1007/978-3-319-19857-6_7

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