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

Université de Rennes 2
MST-8, Los Alamos National Laboratory
Sorbonne Université

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

Résumé

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.

langue originale	Anglais
titre	Modeling and Stochastic Learning for Forecasting in High Dimensions
rédacteurs en chef	Jean-Michel Poggi, Antoniadis Antoniadis, Xavier Brossat
Editeur	Springer Science and Business Media, LLC
Pages	79-93
Nombre de pages	15
ISBN (Electronique)	9783319187310
Les DOIs	https://doi.org/10.1007/978-3-319-18732-7_5
état	Publié - 1 janv. 2015
Modification externe	Oui
Evénement	2nd Workshop on Industry and Practices for Forecasting, WIPFOR 2013 - Paris, France Durée: 5 juin 2013 → 7 juin 2013

Série de publications

Nom	Lecture Notes in Statistics
Volume	217
ISSN (imprimé)	0930-0325
ISSN (Electronique)	2197-7186

Une conférence

Une conférence	2nd Workshop on Industry and Practices for Forecasting, WIPFOR 2013
Pays/Territoire	France
La ville	Paris
période	5/06/13 → 7/06/13

SDG des Nations Unies

Ce résultat contribue à ou aux Objectifs de développement durable suivants

SDG 7 Énergie abordable et propre

Accès au document

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

Contient cette citation

Cornillon, P. A., Hengartner, N., Lefieux, V., & Matzner-Løber, E. (2015). Fully nonparametric short term forecasting electricity consumption. Dans J.-M. Poggi, A. Antoniadis, & X. Brossat (eds.), Modeling and Stochastic Learning for Forecasting in High Dimensions (p. 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.",

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Cornillon, PA, Hengartner, N, Lefieux, V & Matzner-Løber, E 2015, Fully nonparametric short term forecasting electricity consumption. Dans 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, p. 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).

Résultats de recherche: Le chapitre dans un livre, un rapport, une anthologie ou une collection › Contribution à une conférence › Revue par des pairs

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.

U2 - 10.1007/978-3-319-18732-7_5

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

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Cornillon PA, Hengartner N, Lefieux V, Matzner-Løber E. Fully nonparametric short term forecasting electricity consumption. Dans Poggi JM, Antoniadis A, Brossat X, rédacteurs en chef, Modeling and Stochastic Learning for Forecasting in High Dimensions. Springer Science and Business Media, LLC. 2015. p. 79-93. (Lecture Notes in Statistics). doi: 10.1007/978-3-319-18732-7_5

Fully nonparametric short term forecasting electricity consumption

Résumé

Série de publications

Une conférence

SDG des Nations Unies

Accès au document

Empreinte digitale

Contient cette citation