A Note on Averaging Day-Ahead Electricity Price Forecasts Across Calibration Windows

被引：63

作者：

Hubicka, Katarzyna ^{[1
]}

Marcjasz, Grzegorz ^{[2
]}

Weron, Rafal ^{[2
]}

机构：

[1] Wroclaw Univ Sci & Technol, Fac Pure & Appl Math, PL-50370 Wroclaw, Poland

[2] Wroclaw Univ Sci & Technol, Dept Operat Res, Fac Comp Sci & Management, PL-50370 Wroclaw, Poland

来源：

IEEE TRANSACTIONS ON SUSTAINABLE ENERGY | 2019年 / 10卷 / 01期

关键词：

Electricity price forecasting; combining forecasts; calibration window; autoregression; NARX neural network; committee machine; Diebold-Mariano test;

D O I：

10.1109/TSTE.2018.2869557

中图分类号：

X [环境科学、安全科学];

学科分类号：

08 ; 0830 ;

摘要：

We propose a novel concept in energy forecasting and show that averaging day-ahead electricity price forecasts of a predictive model across 28-728 day calibration windows yields better results than selecting only one "optimal" window length. Even more significant accuracy gains can be achieved by averaging over a few, carefully selected windows.

引用

页码：321 / 323

页数：3

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