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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 208964, 9 pages
Research Article

A New Strategy for Short-Term Load Forecasting

1School of Information Science and Engineering, Lanzhou University, Lanzhou, Gansu 730000, China
2School of Mathematics and Statistics, Lanzhou University, Lanzhou 730000, China

Received 28 February 2013; Accepted 22 April 2013

Academic Editor: Fuding Xie

Copyright © 2013 Yi Yang 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 [5 citations]

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

  • Ming Meng, Wei Shang, and Dongxiao Niu, “Monthly Electric Energy Consumption Forecasting Using Multiwindow Moving Average and Hybrid Growth Models,” Journal of Applied Mathematics, vol. 2014, pp. 1–7, 2014. View at Publisher · View at Google Scholar
  • Che-Jung Chang, Jan-Yan Lin, and Meng-Jen Chang, “Extended modeling procedure based on the projected sample for forecasting short-term electricity consumption,” Advanced Engineering Informatics, vol. 30, no. 2, pp. 211–217, 2016. View at Publisher · View at Google Scholar
  • Yun-luo Yu, Wei Li, De-ren Sheng, and Jian-hong Chen, “A hybrid short-term load forecasting method based on improved ensemble empirical mode decomposition and back propagation neural network,” Journal Of Zhejiang University-Science A, vol. 17, no. 2, pp. 101–114, 2016. View at Publisher · View at Google Scholar
  • Agostino Tarsitano, and Ilaria L. Amerise, “Short-term load forecasting using a two-stage sarimax model,” Energy, vol. 133, pp. 108–114, 2017. View at Publisher · View at Google Scholar
  • Ömer Özgur Bozkurt, Göksel Biricik, and Ziya Cihan Taysi, “Artificial neural network and SARIMA based models for power load forecasting in Turkish electricity market Ö,” PLoS ONE, vol. 12, no. 4, 2017. View at Publisher · View at Google Scholar