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Abstract and Applied Analysis
Volume 2013 (2013), Article ID 208964, 9 pages
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 [3 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.
- 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.
- 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.