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Mathematical Problems in Engineering
Volume 2014, Article ID 712417, 17 pages
http://dx.doi.org/10.1155/2014/712417
Research Article

Swarm Intelligence-Based Hybrid Models for Short-Term Power Load Prediction

1School of Statistics, Dongbei University of Finance and Economics, Dalian 116025, China
2School of Mathematics & Statistics, Lanzhou University, Lanzhou 73000, China
3MOE Key Laboratory of Western China’s Environmental Systems, Research School of Arid Environment & Climate Change, Lanzhou University, Lanzhou 73000, China

Received 6 June 2014; Revised 18 July 2014; Accepted 1 August 2014; Published 30 September 2014

Academic Editor: Fang Zong

Copyright © 2014 Jianzhou Wang 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 [11 citations]

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

  • M. Lopez, S. Valero, C Senabre, and A. Gabaldon, “Analysis of the influence of meteorological variables on real-time Short-Term Load Forecasting in Balearic Islands,” 2017 11th IEEE International Conference on Compatibility, Power Electronics and Power Engineering (CPE-POWERENG), pp. 10–15, . View at Publisher · View at Google Scholar
  • M. Lopez, S. Valero, and C. Senabre, “Short-term load forecasting of multiregion systems using mixed effects models,” 2017 14th International Conference on the European Energy Market (EEM), pp. 1–5, . View at Publisher · View at Google Scholar
  • Jian-Long Kuo, and Meng-Ti Chang, “Multiobjective Design of Turbo Injection Mode for Axial Flux Motor in Plastic Injection Molding Machine by Particle Swarm Optimization,” Mathematical Problems in Engineering, vol. 2015, pp. 1–11, 2015. View at Publisher · View at Google Scholar
  • Lida Barba, and Nibaldo Rodríguez, “Hybrid Models Based on Singular Values and Autoregressive Methods for Multistep Ahead Forecasting of Traffic Accidents,” Mathematical Problems in Engineering, vol. 2016, pp. 1–14, 2016. View at Publisher · View at Google Scholar
  • Pauline Ong, and S. Kohshelan, “Performances of Adaptive Cuckoo Search Algorithm in Engineering Optimization,” Handbook of Research on Modern Optimization Algorithms and Applications in Engineering and Economics, pp. 676–699, 2016. View at Publisher · View at Google Scholar
  • Lopez, Sans, Gabaldon, Valero, and Senabre, “Comparison of short-term load forecasting performance by neural network and autoregressive based models,” International Conference on the European Energy Market, EEM, vol. 2018-, 2018. View at Publisher · View at Google Scholar
  • Miguel López, Carlos Sans, Sergio Valero, and Carolina Senabre, “Empirical Comparison of Neural Network and Auto-Regressive Models in Short-Term Load Forecasting,” Energies, vol. 11, no. 8, pp. 2080, 2018. View at Publisher · View at Google Scholar
  • Jui-Sheng Chou, and Thi-Kha Nguyen, “Forward Forecast of Stock Price Using Sliding-Window Metaheuristic-Optimized Machine-Learning Regression,” IEEE Transactions on Industrial Informatics, vol. 14, no. 7, pp. 3132–3142, 2018. View at Publisher · View at Google Scholar
  • Imran Rahman, and Junita Mohamad-Saleh, “Hybrid Bio-Inspired Computational Intelligence Techniques for Solving Power System Optimization Problems: A Comprehensive Survey,” Applied Soft Computing, 2018. View at Publisher · View at Google Scholar
  • Sergio Valero, Ana Rodriguez, Iago Veiras, Carolina Senabre, and Miguel López, “New online load forecasting system for the Spanish Transport System Operator,” Electric Power Systems Research, vol. 154, pp. 401–412, 2018. View at Publisher · View at Google Scholar
  • Jui-Sheng Chou, and Thi Thu Ha Truong, “Sliding-window metaheuristic optimization-based forecast system for foreign exchange analysis,” Soft Computing, 2019. View at Publisher · View at Google Scholar