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Short Term Load Forecasting in the Industry for Establishing Consumption Baselines: A French Case Read chapter Read first chapter

2015 | OriginalPaper | Chapter

Fully Nonparametric Short Term Forecasting Electricity Consumption

Authors : Pierre-André Cornillon, Nick Hengartner, Vincent Lefieux, Eric Matzner-Løber

Published in: Modeling and Stochastic Learning for Forecasting in High Dimensions

Publisher: Springer International Publishing

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Abstract

Electricity Transmission System Operators (TSO) are responsible for operating, maintaining and developing the high and extra high voltage network. They guarantee the reliability and proper operation of the power network. Anticipating electricity demand helps to guarantee the balance between generation and consumption at all times, and directly influences the reliability of the power system. In this paper, we focus on predicting short term electricity consumption in France. Several competitors such as iterative bias reduction, functional nonparametric model or non-linear additive autoregressive approach are compared to the actual SARIMA method. Our results show that iterative bias reduction approach outperforms all competitors both on Mean Absolute Percentage Error and on the percentage of forecast errors higher than 2,000 MW.

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previous chapter Constructing Graphical Models via the Focused Information Criterion
next chapter Forecasting Electricity Consumption by Aggregating Experts; How to Design a Good Set of Experts
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Metadata
Title
Fully Nonparametric Short Term Forecasting Electricity Consumption
Authors
Pierre-André Cornillon
Nick Hengartner
Vincent Lefieux
Eric Matzner-Løber
Copyright Year
2015
Book
Modeling and Stochastic Learning for Forecasting in High Dimensions

Print ISBN: 978-3-319-18731-0

Electronic ISBN: 978-3-319-18732-7

Copyright Year: 2015

DOI
https://doi.org/10.1007/978-3-319-18732-7_5

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