Fully Nonparametric Short Term Forecasting Electricity Consumption

被引：1

作者：

Cornillon, Pierre-Andre ^{[1
]}

Hengartner, Nick ^{[2
]}

Lefieux, Vincent ^{[3
,4
]}

Matzner-Lober, Eric ^{[1
,5
]}

机构：

[1] Univ Rennes 2, Rennes, France

[2] Los Alamos Natl Lab, Los Alamos, NM USA

[3] RTE EPT, Paris, France

[4] UPMC ISUP, Paris, France

[5] Agrocampus Ouest, Rennes, France

来源：

MODELING AND STOCHASTIC LEARNING FOR FORECASTING IN HIGH DIMENSIONS | 2015年 / 217卷

关键词：

SMOOTHING PARAMETER SELECTION; GENERALIZED CROSS-VALIDATION; MODELS;

D O I：

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

中图分类号：

O29 [应用数学];

学科分类号：

070104 ;

摘要：

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.

引用

页码：79 / 93

页数：15

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