Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2013, Article ID 194730, 8 pages
http://dx.doi.org/10.1155/2013/194730
Research Article

Forecasting Electrical Energy Consumption of Equipment Maintenance Using Neural Network and Particle Swarm Optimization

College of Field Engineering, PLA University of Science and Technology, Nanjing 210007, China

Received 1 June 2013; Revised 17 August 2013; Accepted 9 September 2013

Academic Editor: Yi-Chung Hu

Copyright © 2013 Xunlin Jiang 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.

Linked References

  1. Z. Q. Zhang, J. P. Wang, X. Y. Duan et al., Introduction To Equipment Technical Support, Military Science Press, Beijing, China, 2001.
  2. P. Crompton and Y. Wu, “Energy consumption in China: past trends and future directions,” Energy Economics, vol. 27, no. 1, pp. 195–208, 2005. View at Publisher · View at Google Scholar · View at Scopus
  3. D. Niu and M. Meng, “Research on seasonal increasing electric energy demand forecasting: a case in China,” Chinese Journal of Management Science, vol. 18, no. 2, pp. 108–112, 2010. View at Google Scholar
  4. N. Wada, O. Saito, Y. Yamamoto, T. Morioka, and A. Tokai, “Evaluating effects on the flow of electrical and electronic equipment and energy consumption due to alternative consumption patterns in China,” Journal of Risk Research, vol. 15, no. 1, pp. 107–130, 2012. View at Publisher · View at Google Scholar · View at Scopus
  5. H. K. AlFares and M. Nazeeruddin, “Lectric load forecasting: literature survey and classification of methods,” International Journal of Systems Science, vol. 33, no. 1, pp. 23–24, 2002. View at Publisher · View at Google Scholar
  6. G. A. N. Mbamalu and M. E. El-Hawary, “Load forecasting via suboptimal seasonal autoregressive models and iteratively reweighted least squares estimation,” IEEE Transactions on Power Systems, vol. 8, no. 1, pp. 343–348, 1993. View at Publisher · View at Google Scholar · View at Scopus
  7. T. Haida and S. Muto, “Regression based peak load forecasting using a transformation technique,” IEEE Transactions on Power Systems, vol. 9, no. 4, pp. 1788–1794, 1994. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Al-Shehri, “A simple forecasting model for industrial electric energy consumption,” International Journal of Energy Research, vol. 24, no. 8, pp. 719–726, 2000. View at Publisher · View at Google Scholar · View at Scopus
  9. H. S. Hippert, C. E. Pedreira, and R. C. Souza, “Neural networks for short-term load forecasting: a review and evaluation,” IEEE Transactions on Power Systems, vol. 16, no. 1, pp. 44–55, 2001. View at Publisher · View at Google Scholar · View at Scopus
  10. K.-H. Kim, J.-K. Park, K.-J. Hwang, and S.-H. Kim, “Implementation of hybrid short-term load forecasting system using artificial neural networks and fuzzy expert systems,” IEEE Transactions on Power Systems, vol. 10, no. 3, pp. 1534–1539, 1995. View at Publisher · View at Google Scholar · View at Scopus
  11. K. Chau, “A review on the integration of artificial intelligence into coastal modeling,” Journal of Environmental Management, vol. 80, no. 1, pp. 47–57, 2006. View at Publisher · View at Google Scholar · View at Scopus
  12. D. E. Rumelhart, G. E. Hinton, and R. J. Williams, “Learning representations by back-propagating errors,” Nature, vol. 323, no. 6088, pp. 533–536, 1986. View at Publisher · View at Google Scholar · View at Scopus
  13. S. Chen and Z. Wang, “Acceleration strategies in generalized belief propagation,” IEEE Transactions on Industrial Informatics, vol. 8, no. 1, pp. 41–48, 2012. View at Publisher · View at Google Scholar · View at Scopus
  14. G. F. Miller, P. M. Todd, and S. U. Hegde, “Designing neural networks using genetic algorithms,” in Proceedings of the 3rd International Conference on Genetic Algorithms, J. David Schaffer, Ed., pp. 379–384, Morgan Kaufmann Publishers, San Francisco, Calif, USA, 1989. View at Publisher · View at Google Scholar
  15. C. Zhang, H. Shao, and Y. Li, “Particle swarm optimization for evolving artificial neural network,” in Proceedings of the IEEE International Conference on Systems, Man and Cybernetics, vol. 4, pp. 2487–2490, October 2000. View at Scopus
  16. J. D. Schaffer, D. Whitley, and L. J. Eshelman, “Combinations of genetic algorithms and neural networks: a survey of the state of the art,” in Proceedings of the International Workshop on Combinations of Genetic Algorithms and Neural Networks (COGANN '92), pp. 1–37, 1992. View at Publisher · View at Google Scholar
  17. M. Settles, B. Rodebaugh, and T. Soule, “Comparison of genetic algorithm and particle swarm optimizer when evolving a recurrent neural network,” in Genetic and Evolutionary Computation, E. Cantú-Paz, J. A. Foster, K. Deb et al., Eds., vol. 2723 of Lecture Notes in Computer Science, pp. 148–149, Springer, New York, NY, USA, 2003. View at Google Scholar
  18. M. Kawashima, “Artificial neural network backpropagation model with three-phase annealing developed for the building energy predictor shootout,” ASHRAE Transactions, vol. 100, no. 2, pp. 1096–1103, 1994. View at Google Scholar
  19. S. M. Islam, S. M. Al-Alawi, and K. A. Ellithy, “Forecasting monthly electric load and energy for a fast growing utility using an artificial neural network,” Electric Power Systems Research, vol. 34, no. 1, pp. 1–9, 1995. View at Publisher · View at Google Scholar · View at Scopus
  20. A. Al-Shehri, “Artificial neural network for forecasting residential electrical energy,” International Journal of Energy Research, vol. 23, no. 8, pp. 649–661, 1999. View at Publisher · View at Google Scholar · View at Scopus
  21. N. D. Hatziargyriou and E. N. Dialynas, “An optimized adaptive neural network for annual midterm energy forecasting,” IEEE Transactions on Power Systems, vol. 21, no. 1, pp. 385–391, 2006. View at Google Scholar
  22. A. Sözen, M. A. Akçayol, and E. Arcaklioğlu, “Forecasting net energy consumption using artificial neural network,” Energy Sources B, vol. 1, no. 2, pp. 147–155, 2006. View at Publisher · View at Google Scholar
  23. K. Ermis, A. Midilli, I. Dincer, and M. A. Rosen, “Artificial neural network analysis of world green energy use,” Energy Policy, vol. 35, no. 3, pp. 1731–1743, 2007. View at Publisher · View at Google Scholar · View at Scopus
  24. A. Azadeh, S. F. Ghaderi, and S. Sohrabkhani, “A simulated-based neural network algorithm for forecasting electrical energy consumption in Iran,” Energy Policy, vol. 36, no. 7, pp. 2637–2644, 2008. View at Publisher · View at Google Scholar · View at Scopus
  25. R. Yokoyama, T. Wakui, and R. Satake, “Prediction of energy demands using neural network with model identification by global optimization,” Energy Conversion and Management, vol. 50, no. 2, pp. 319–327, 2009. View at Publisher · View at Google Scholar · View at Scopus
  26. Z. W. Geem and W. E. Roper, “Energy demand estimation of South Korea using artificial neural network,” Energy Policy, vol. 37, no. 10, pp. 4049–4054, 2009. View at Publisher · View at Google Scholar · View at Scopus
  27. S. Haykin, Neural Networks: A Comprehensive Foundation, Prentice-Hall, Englewood Cliffs, NJ, USA, 1994.
  28. J. Kennedy, “Bare bones particle swarms,” in Proceedings of the IEEE Swarm Intelligence Symposium, pp. 80–87, 2003.
  29. N. M. Kwok, D. K. Liu, K. C. Tan, and Q. P. Ha, “An empirical study on the settings of control coefficients in particle swarm optimization,” in Proceedings of the IEEE Congress on Evolutionary Computation (CEC '06), pp. 3165–3172, Vancouver, Canada, July 2006. View at Scopus
  30. Z. H. Zhan, J. Zhang, Y. Li, and H. S. H. Chung, “Adaptive particle swarm optimization,” IEEE Transactions on Systems, Man, and Cybernetics B, vol. 39, no. 6, pp. 1362–1381, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. Y. J. Zheng, Q. Song, and S. Y. Chen, “Multiobjective fireworks optimization for variable-rate fertilization in oil crop production,” Applied Soft Computing, vol. 13, no. 11, pp. 4253–4263, 2013. View at Publisher · View at Google Scholar
  32. P. Lu, S. Chen, and Y. Zheng, “Artificial intelligence in civil engineering,” Mathematical Problems in Engineering, vol. 2012, Article ID 145974, 22 pages, 2012. View at Publisher · View at Google Scholar
  33. Y. J. Zheng, S. Y. Chen, Y. Lin, and W. L. Wang, “Bio-inspired optimization of sustainable energy systems: a review,” Mathematical Problems in Engineering, vol. 2013, Article ID 354523, 12 pages, 2013. View at Publisher · View at Google Scholar
  34. Y. J. Zheng and S. Y. Chen, “Cooperative particle swarm optimization for multiobjective transportation planning,” Applied Intelligence, vol. 39, no. 1, pp. 202–216, 2013. View at Publisher · View at Google Scholar
  35. X. Yang, J. Yuan, and H. Mao, “A modified particle swarm optimizer with dynamic adaptation,” Applied Mathematics and Computation, vol. 189, no. 2, pp. 1205–1213, 2007. View at Publisher · View at Google Scholar · View at Zentralblatt MATH · View at Scopus
  36. M. S. Arumugam and M. V. C. Rao, “On the improved performances of the particle swarm optimization algorithms with adaptive parameters, cross-over operators and root mean square (RMS) variants for computing optimal control of a class of hybrid systems,” Applied Soft Computing Journal, vol. 8, no. 1, pp. 324–336, 2008. View at Publisher · View at Google Scholar · View at Scopus
  37. B. K. Panigrahi, V. R. Pandi, and S. Das, “Adaptive particle swarm optimization approach for static and dynamic economic load dispatch,” Energy Conversion and Management, vol. 49, no. 6, pp. 1407–1415, 2008. View at Publisher · View at Google Scholar · View at Scopus
  38. A. Nickabadi, M. M. Ebadzadeh, and R. Safabakhsh, “A novel particle swarm optimization algorithm with adaptive inertia weight,” Applied Soft Computing Journal, vol. 11, no. 4, pp. 3658–3670, 2011. View at Publisher · View at Google Scholar · View at Scopus
  39. A. Ratnaweera, S. K. Halgamuge, and H. C. Watson, “Self-organizing hierarchical particle swarm optimizer with time-varying acceleration coefficients,” IEEE Transactions on Evolutionary Computation, vol. 8, no. 3, pp. 240–255, 2004. View at Publisher · View at Google Scholar · View at Scopus
  40. Y. J. Zheng, H. F. Ling, and Q. Guan, “Adaptive parameters for a modified comprehensive learning particle swarm optimizer,” Mathematical Problems in Engineering, vol. 2012, Article ID 207318, 11 pages, 2012. View at Publisher · View at Google Scholar
  41. M. R. AlRashidi and K. M. EL-Naggar, “Long term electric load forecasting based on particle swarm optimization,” Applied Energy, vol. 87, no. 1, pp. 320–326, 2010. View at Publisher · View at Google Scholar · View at Scopus
  42. J. S. R. Jang, “ANFIS: adaptive-network-based fuzzy inference system,” IEEE Transactions on Systems, Man and Cybernetics, vol. 23, no. 3, pp. 665–685, 1993. View at Publisher · View at Google Scholar · View at Scopus
  43. A. R. Solis and G. Panoutsos, “Granular computing neural-fuzzy modelling: a neutrosophic approach,” Applied Soft Computing, vol. 13, no. 9, pp. 4010–4021, 2012. View at Google Scholar
  44. K. Wang and Y. J. Zheng, “A new particle swarm optimization algorithm for fuzzy optimization of armored vehicle scheme design,” Applied Intelligence, vol. 37, no. 4, pp. 520–526, 2012. View at Publisher · View at Google Scholar · View at Scopus
  45. Y. Zheng, H. Shi, and S. Chen, “Fuzzy combinatorial optimization with multiple ranking criteria: a staged tabu search framework,” Pacific Journal of Optimization, vol. 8, no. 3, pp. 457–472, 2012. View at Google Scholar · View at Zentralblatt MATH
  46. Y. J. Zheng and H. F. Ling, “Emergency transportation planning in disaster relief supply chain management: a cooperative fuzzy optimization approach,” Soft Computing, vol. 17, no. 7, pp. 1301–1314, 2013. View at Publisher · View at Google Scholar