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Electricity load forecasting and backcasting with semi-parametric models

Raphaël Nedellec 1 Jairo Cugliari 2 Yannig Goude 1
2 SELECT - Model selection in statistical learning
LMO - Laboratoire de Mathématiques d'Orsay, Inria Saclay - Ile de France
Abstract : We sum up the methodology of the team tololo for the Global Energy Forecasting Competition 2012: Load Forecasting. Our strategy consisted of a temporal multi-scale model that combines three components. The first component was a long term trend estimated by means of non-parametric smoothing. The second was a medium term component describing the sensitivity of the electricity demand to the temperature at each time step. We use a generalized additive model to fit this component, using calendar information as well. Finally, a short term component models local behaviours. As the factors that drive this component are unknown, we use a random forest model to estimate it.
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https://hal.inria.fr/hal-00942688
Contributor : Erwan Le Pennec <>
Submitted on : Thursday, February 6, 2014 - 11:36:12 AM
Last modification on : Wednesday, September 16, 2020 - 5:05:02 PM

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Raphaël Nedellec, Jairo Cugliari, Yannig Goude. Electricity load forecasting and backcasting with semi-parametric models. International Journal of Forecasting, Elsevier, 2013, 30 (2), pp.375-381. ⟨10.1016/j.ijforecast.2013.07.004⟩. ⟨hal-00942688⟩

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