Get 20M+ Full-Text Papers For Less Than $1.50/day. Start a 14-Day Trial for You or Your Team.

Learn More →

A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting

A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption... We propose a novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting. Our method is based on the principles of “data-characteristic-based modeling” and “decomposition and ensemble”. The model improves on existing decomposition ensemble learning techniques (with “decomposition and ensemble”) by using “data-characteristic-based modeling” to forecast the decomposed modes. Ensemble empirical mode decomposition is first used to decompose the original nuclear energy consumption data into a series of comparatively simple modes, reducing the complexity of the data. Then, the extracted modes are thoroughly analyzed to capture hidden data characteristics. These characteristics are used to determine appropriate forecasting models for each mode. Final forecasts are obtained by combining these predicted components using an effective ensemble tool, such as least squares support vector regression. For illustration and verification purposes, we have implemented the proposed model to forecast nuclear energy consumption in China. Our numerical results demonstrate that the novel method significantly outperforms all considered benchmarks. This indicates that it is a very promising tool for forecasting complex and irregular data such as nuclear energy consumption. http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png Annals of Operations Research Springer Journals

A novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting

Download PDF
22 pages

Loading next page...
 
/lp/springer-journals/a-novel-mode-characteristic-based-decomposition-ensemble-model-for-bJjC9oZlTi

References (49)

Publisher
Springer Journals
Copyright
Copyright © 2014 by Springer Science+Business Media New York
Subject
Economics / Management Science; Operations Research/Decision Theory; Combinatorics; Theory of Computation
ISSN
0254-5330
eISSN
1572-9338
DOI
10.1007/s10479-014-1595-5
Publisher site
See Article on Publisher Site

Abstract

We propose a novel mode-characteristic-based decomposition ensemble model for nuclear energy consumption forecasting. Our method is based on the principles of “data-characteristic-based modeling” and “decomposition and ensemble”. The model improves on existing decomposition ensemble learning techniques (with “decomposition and ensemble”) by using “data-characteristic-based modeling” to forecast the decomposed modes. Ensemble empirical mode decomposition is first used to decompose the original nuclear energy consumption data into a series of comparatively simple modes, reducing the complexity of the data. Then, the extracted modes are thoroughly analyzed to capture hidden data characteristics. These characteristics are used to determine appropriate forecasting models for each mode. Final forecasts are obtained by combining these predicted components using an effective ensemble tool, such as least squares support vector regression. For illustration and verification purposes, we have implemented the proposed model to forecast nuclear energy consumption in China. Our numerical results demonstrate that the novel method significantly outperforms all considered benchmarks. This indicates that it is a very promising tool for forecasting complex and irregular data such as nuclear energy consumption.

Journal

Annals of Operations Research – Springer Journals

Published: Apr 28, 2014

There are no references for this article.