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Journal of Applied Mathematics
Volume 2014, Article ID 243171, 7 pages
http://dx.doi.org/10.1155/2014/243171
Research Article

Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models

1School of Economics and Management, North China Electric Power University, No. 619, Yonghua Street, Baoding, Hebei 071003, China
2Soft Science Research Base of Hebei Province, North China Electric Power University, Baoding, Hebei 071003, China
3School of Economics, Hebei University, Baoding, Hebei 071002, China

Received 24 December 2013; Accepted 5 May 2014; Published 15 May 2014

Academic Editor: Shan Zhao

Copyright © 2014 Ming Meng 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.

Citations to this Article [3 citations]

The following is the list of published articles that have cited the current article.

  • Chia-Yu Shen, and Hsiao-Fan Wang, “Electricity load forecasting in a smart grid system,” Intelligent Data Analysis, vol. 20, no. 5, pp. 1223–1242, 2016. View at Publisher · View at Google Scholar
  • Siwei Wei, and Ting Wang, “Electricity consumption scenario prediction based on factor analysis and least squares support vector machine optimised by fruit fly algorithm,” International Journal of Applied Decision Sciences, vol. 10, no. 3, pp. 254–273, 2017. View at Publisher · View at Google Scholar
  • J. T. Lalis, B. D. Gerardo, and Yungcheol Byun, “A Mechanism for Online and Dynamic Forecasting of Monthly Electric Load Consumption Using Parallel Adaptive Multilayer Perceptron (PAMLP),” Computer and Information Science, vol. 791, pp. 177–188, 2018. View at Publisher · View at Google Scholar