Forecasting natural gas consumption using Bagging and modified regularization techniques

被引：32

作者：

Meira, Erick ^{[1
,2
]}

Cyrino Oliveira, Fernando Luiz ^{[1
]}

de Menezes, Lilian M. ^{[3
]}

机构：

[1] Pontifical Catholic Univ Rio de Janeiro, Dept Ind Engn, Rua Marques Sao Vicente,225,Ed Cardeal Leme, BR-22451900 Rio De Janeiro, Brazil

[2] Brazilian Agcy Res & Innovat Finep, Energy Informat Technol & Serv Div, Praia Flamengo 200,9 Andar, BR-22210030 Rio De Janeiro, Brazil

[3] City Univ London, Bayes Business Sch, Cass, London EC1Y 8TZ, England

来源：

ENERGY ECONOMICS | 2022年 / 106卷

关键词：

Forecasting; Natural gas demand; Ensembles; Bagging; Regularization; TIME-SERIES; NEURAL-NETWORKS; ENERGY-CONSUMPTION; DECOMPOSITION; COMBINATION; REGRESSION; BOOTSTRAP; ENSEMBLE; COMPETITION; MODELS;

D O I：

10.1016/j.eneco.2021.105760

中图分类号：

F [经济];

学科分类号：

02 ;

摘要：

This paper develops a new approach to forecast natural gas consumption via ensembles. It combines Bootstrap Aggregation (Bagging), univariate time series forecasting methods and modified regularization routines. A new variant of Bagging is introduced, which uses Maximum Entropy Bootstrap (MEB) and a modified regularization routine that ensures that the data generating process is kept in the ensemble. Monthly natural gas consumption time series from 18 European countries are considered. A comparative, out-of-sample evaluation is conducted up to 12 steps (a year) ahead, using a comprehensive set of competing forecasting approaches. These range from statistical benchmarks to machine learning methods and state-of-the-art ensembles. Several performance (accuracy) metrics are used, and a sensitivity analysis is undertaken. Overall, the new variant of Bagging is flexible, reliable, and outperforms well-established approaches. Consequently, it is suitable to support decision making in the energy and other sectors.

引用

页数：23