Forecasting the electricity consumption by aggregating specialized experts; a review of the sequential aggregation of specialized experts, with an application to Slovakian and French country-wide one-day-ahead (half-)hourly predictions

Marie Devaine 1 Yannig Goude 2 Gilles Stoltz 1, 3, 4, *
* Corresponding author
4 CLASSIC - Computational Learning, Aggregation, Supervised Statistical, Inference, and Classification
DMA - Département de Mathématiques et Applications - ENS Paris, ENS Paris - École normale supérieure - Paris, Inria Paris-Rocquencourt
Abstract : We consider a variant of the sequential prediction of arbitrary sequences based on experts advice, called prediction with specialized experts. We design aggregation rules, that sequentially combine the forecasts provided by the experts; the latter are specialized and need not output a prediction at all time instances while the aggregation rules have to. We provide first a review of the literature on specialized experts and take a new look at some aggregation rules (obtained as adaptations or extensions of earlier rules). We then consider an application to the sequential short-term (one-day-ahead) forecasting of electricity consumption; to do so, we consider two data sets, a Slovakian one and a French one, respectively concerned with hourly and half-hourly predictions. We introduce and develop a general methodology to perform the stated empirical studies. The introduced aggregation rules demonstrate an improved accuracy on the data sets at hand; the improvements lie in a reduced mean squared error but also in a more robust behavior with respect to large occasional errors.
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https://hal.archives-ouvertes.fr/hal-00484940
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Submitted on : Sunday, March 27, 2011 - 2:21:54 PM
Last modification on : Friday, May 25, 2018 - 12:02:06 PM
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Marie Devaine, Yannig Goude, Gilles Stoltz. Forecasting the electricity consumption by aggregating specialized experts; a review of the sequential aggregation of specialized experts, with an application to Slovakian and French country-wide one-day-ahead (half-)hourly predictions. 2011. ⟨hal-00484940v2⟩

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