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The Scientific World Journal
Volume 2014, Article ID 301032, 12 pages
http://dx.doi.org/10.1155/2014/301032
Research Article

A Grey Self-Memory Coupling Prediction Model for Energy Consumption Prediction

1College of Economics and Management, Nanjing University of Aeronautics and Astronautics, Nanjing 211106, China
2School of Science, Nantong University, Nantong 226019, China
3School of Electrical and Computer Engineering, Cornell University, Ithaca, NY 14853, USA

Received 6 January 2014; Revised 6 May 2014; Accepted 21 May 2014; Published 18 June 2014

Academic Editor: Constantin Papaodysseus

Copyright © 2014 Xiaojun Guo et al. This is an open access article distributed under the Creative Commons Attribution License, which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

Abstract

Energy consumption prediction is an important issue for governments, energy sector investors, and other related corporations. Although there are several prediction techniques, selection of the most appropriate technique is of vital importance. As for the approximate nonhomogeneous exponential data sequence often emerging in the energy system, a novel grey self-memory coupling prediction model is put forward in order to promote the predictive performance. It achieves organic integration of the self-memory principle of dynamic system and grey model. The traditional grey model’s weakness as being sensitive to initial value can be overcome by the self-memory principle. In this study, total energy, coal, and electricity consumption of China is adopted for demonstration by using the proposed coupling prediction technique. The results show the superiority of self-memory coupling prediction model when compared with the results from the literature. Its excellent prediction performance lies in that the proposed coupling model can take full advantage of the systematic multitime historical data and catch the stochastic fluctuation tendency. This work also makes a significant contribution to the enrichment of grey prediction theory and the extension of its application span.