ENBIS-17 in Naples

9 – 14 September 2017; Naples (Italy) Abstract submission: 21 November 2016 – 10 May 2017

Additive Quantile Regression for Electricity Load Forecasting

12 September 2017, 09:20 – 09:40

Abstract

Submitted by
Matteo Fasiolo
Authors
Matteo Fasiolo (University of Bristol), Simon N. Wood (University of Bristol), Yannig Goude (EDF R&D), Raphael Nedellec (EDF R&D)
Abstract
Energy utilities need accurate electricity load predictions for the purpose of production planning. Generalized additive models are useful tools in this context, because they offer a compromise between flexibility and interpretability. However, it is sometimes difficult to find an adequate parametric model for the conditional distribution of the response. In addition, production planning might require estimates of only some conditional quantiles of electricity load, rather than an approximation to the whole distribution. Hence, for this application, quantile regression might represent an interesting alternative to distributional approaches. In this talk we will describe how the GAM framework can be used to fit additive quantile regression, in a stable and computationally efficient manner. We will also present an R package, called qgam, which can be used to fit quantile GAMS, and we will show how the proposed method's performance compares with that of alternative methods, in the context of electricity load forecasting.
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