Fully nonparametric short term forecasting electricity consumption

Pierre André Cornillon; Nick Hengartner; Vincent Lefieux; Eric Matzner-Løber

doi:10.1007/978-3-319-18732-7_5

Fully nonparametric short term forecasting electricity consumption

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

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

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 2,000MW.

Original language	English
Title of host publication	Modeling and Stochastic Learning for Forecasting in High Dimensions
Editors	Jean-Michel Poggi, Antoniadis Antoniadis, Xavier Brossat
Publisher	Springer Science and Business Media, LLC
Pages	79-93
Number of pages	15
ISBN (Electronic)	9783319187310
DOIs	https://doi.org/10.1007/978-3-319-18732-7_5
Publication status	Published - 1 Jan 2015
Externally published	Yes
Event	2nd Workshop on Industry and Practices for Forecasting, WIPFOR 2013 - Paris, France Duration: 5 Jun 2013 → 7 Jun 2013

Publication series

Name	Lecture Notes in Statistics
Volume	217
ISSN (Print)	0930-0325
ISSN (Electronic)	2197-7186

Conference

Conference	2nd Workshop on Industry and Practices for Forecasting, WIPFOR 2013
Country/Territory	France
City	Paris
Period	5/06/13 → 7/06/13

UN SDGs

This output contributes to the following UN Sustainable Development Goals (SDGs)

SDG 7 Affordable and Clean Energy

Access to Document

10.1007/978-3-319-18732-7_5

Cite this

Cornillon, P. A., Hengartner, N., Lefieux, V., & Matzner-Løber, E. (2015). Fully nonparametric short term forecasting electricity consumption. In J.-M. Poggi, A. Antoniadis, & X. Brossat (Eds.), Modeling and Stochastic Learning for Forecasting in High Dimensions (pp. 79-93). (Lecture Notes in Statistics; Vol. 217). Springer Science and Business Media, LLC. https://doi.org/10.1007/978-3-319-18732-7_5

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title = "Fully nonparametric short term forecasting electricity consumption",

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 2,000MW.",

author = "Cornillon, \{Pierre Andr{\'e}\} and Nick Hengartner and Vincent Lefieux and Eric Matzner-L{\o}ber",

note = "Publisher Copyright: {\textcopyright} Springer International Publishing Switzerland 2015.; 2nd Workshop on Industry and Practices for Forecasting, WIPFOR 2013 ; Conference date: 05-06-2013 Through 07-06-2013",

year = "2015",

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doi = "10.1007/978-3-319-18732-7\_5",

language = "English",

series = "Lecture Notes in Statistics",

publisher = "Springer Science and Business Media, LLC",

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Cornillon, PA, Hengartner, N, Lefieux, V & Matzner-Løber, E 2015, Fully nonparametric short term forecasting electricity consumption. in J-M Poggi, A Antoniadis & X Brossat (eds), Modeling and Stochastic Learning for Forecasting in High Dimensions. Lecture Notes in Statistics, vol. 217, Springer Science and Business Media, LLC, pp. 79-93, 2nd Workshop on Industry and Practices for Forecasting, WIPFOR 2013, Paris, France, 5/06/13. https://doi.org/10.1007/978-3-319-18732-7_5

Fully nonparametric short term forecasting electricity consumption. / Cornillon, Pierre André; Hengartner, Nick; Lefieux, Vincent et al.
Modeling and Stochastic Learning for Forecasting in High Dimensions. ed. / Jean-Michel Poggi; Antoniadis Antoniadis; Xavier Brossat. Springer Science and Business Media, LLC, 2015. p. 79-93 (Lecture Notes in Statistics; Vol. 217).

Research output: Chapter in Book/Report/Conference proceeding › Conference contribution › peer-review

TY - GEN

T1 - Fully nonparametric short term forecasting electricity consumption

AU - Cornillon, Pierre André

AU - Hengartner, Nick

AU - Lefieux, Vincent

AU - Matzner-Løber, Eric

PY - 2015/1/1

Y1 - 2015/1/1

N2 - 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 2,000MW.

AB - 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 2,000MW.

UR - https://www.scopus.com/pages/publications/84931825468

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DO - 10.1007/978-3-319-18732-7_5

M3 - Conference contribution

AN - SCOPUS:84931825468

T3 - Lecture Notes in Statistics

SP - 79

EP - 93

BT - Modeling and Stochastic Learning for Forecasting in High Dimensions

A2 - Poggi, Jean-Michel

A2 - Antoniadis, Antoniadis

A2 - Brossat, Xavier

PB - Springer Science and Business Media, LLC

T2 - 2nd Workshop on Industry and Practices for Forecasting, WIPFOR 2013

Y2 - 5 June 2013 through 7 June 2013

ER -

Fully nonparametric short term forecasting electricity consumption

Abstract

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UN SDGs

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