Table of Contents Author Guidelines Submit a Manuscript
Table of Contents Alerts

To receive news and publication updates for Mathematical Problems in Engineering, enter your email address in the box below.

Mathematical Problems in Engineering
Volume 2016 (2016), Article ID 3968324, 10 pages
http://dx.doi.org/10.1155/2016/3968324
Research Article

Dynamic Heat Supply Prediction Using Support Vector Regression Optimized by Particle Swarm Optimization Algorithm

Meiping Wang and Qi Tian

School of Environment Science and Engineering, Taiyuan University of Technology, Taiyuan 030024, China

Received 31 December 2015; Revised 30 March 2016; Accepted 11 April 2016

Academic Editor: Antonino Laudani

Copyright © 2016 Meiping Wang and Qi Tian. 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. Q.-W. Zeng, Z.-H. Xu, and J. Wu, “Forecasting of electricity load based on particle swarm optimization and support vector machine,” Microelectronics & Computer, vol. 28, no. 1, pp. 147–153, 2011. View at Google Scholar
  2. N. Lu, B.-L. Wu, and Y. Liu, “Application of support vector machine model in load forecasting based on adaptive particle swarm optimization,” Power System Protection and Control, vol. 39, no. 15, pp. 43–51, 2011. View at Google Scholar · View at Scopus
  3. F. S. Westphal and R. Lamberts, “The use of simplified weather data to estimate thermal loads of non-residential buildings,” Energy and Buildings, vol. 36, no. 8, pp. 847–854, 2004. View at Publisher · View at Google Scholar · View at Scopus
  4. V. D. Stevanovic, B. Zivkovic, S. Prica, B. Maslovaric, V. Karamarkovic, and V. Trkulja, “Prediction of thermal transients in district heating systems,” Energy Conversion and Management, vol. 50, no. 9, pp. 2167–2173, 2009. View at Publisher · View at Google Scholar · View at Scopus
  5. H. Gadd and S. Werner, “Daily heat load variations in Swedish district heating systems,” Applied Energy, vol. 106, pp. 47–55, 2013. View at Publisher · View at Google Scholar · View at Scopus
  6. J. S. Carlos and M. C. S. Nepomuceno, “A simple methodology to predict heating load at an early design stage of dwellings,” Energy and Buildings, vol. 55, pp. 198–207, 2012. View at Publisher · View at Google Scholar · View at Scopus
  7. E. Dotzauer, “Simple model for prediction of loads in district-heating systems,” Applied Energy, vol. 73, no. 3-4, pp. 277–284, 2002. View at Publisher · View at Google Scholar · View at Scopus
  8. K. Wojdyga, “An influence of weather conditions on heat demand in district heating systems,” Energy and Buildings, vol. 40, no. 11, pp. 2009–2014, 2008. View at Publisher · View at Google Scholar · View at Scopus
  9. A. Fouda, Z. Melikyan, M. A. Mohamed, and H. F. Elattar, “A modified method of calculating the heating load for residential buildings,” Energy and Buildings, vol. 75, pp. 170–175, 2014. View at Publisher · View at Google Scholar · View at Scopus
  10. S.-C. Deng, D.-L. Yu, and W.-G. Qi, “Heat load forecasting based on multiplicative seasonal ARIMA model,” Journal of Shenyang University of Technology, vol. 33, no. 3, pp. 321–325, 2011. View at Google Scholar · View at Scopus
  11. J. R. Forrester and W. J. Wepfer, “Formulation of a load-prediction algorithm for a large commercial-building,” ASHRAE Transactions, vol. 90, part 2B, pp. 536–551, 1984. View at Google Scholar
  12. M. Kawashima, C. E. Dorgan, and J. W. Mitchell, “Optimizing system control with load prediction by neural networks for an ice-storage system,” ASHRAE Transactions, Part 1, vol. 102, pp. 1169–1178, 1996. View at Google Scholar
  13. Z. Hou, Z. Lian, Y. Yao, and X. Yuan, “Cooling-load prediction by the combination of rough set theory and an artificial neural-network based on data-fusion technique,” Applied Energy, vol. 83, no. 9, pp. 1033–1046, 2006. View at Publisher · View at Google Scholar · View at Scopus
  14. D. Zhu and Z. Li, “Thermal load prediction for heating systems based on wavelet and neural network,” Journal of Shenyang Jianzhu University (Natural Science), vol. 23, no. 1, pp. 157–160, 2007. View at Google Scholar · View at Scopus
  15. W. Du and Z. Puyan, “The use of thermal load prediction based on gray model in district heating system,” District Heating, no. 2, pp. 32–35, 2010. View at Google Scholar
  16. G. Bingkun, L. Yang, and X. Mingzi, “Application of particle swarm optimization algorithm in the heating load combination forecasting,” Information and Electronic Engineering, vol. 9, no. 5, pp. 655–659, 2011. View at Google Scholar
  17. C. Gaojian, F. Dongsheng, and Q. Yongli, “Load prediction model of district heating system based on Elman neural network,” Building Energy Efficiency, vol. 39, no. 241, pp. 9–11, 2011. View at Google Scholar
  18. K. M. Powell, A. Sriprasad, W. J. Cole, and T. F. Edgar, “Heating, cooling, and electrical load forecasting for a large-scale district energy system,” Energy, vol. 74, pp. 877–885, 2014. View at Publisher · View at Google Scholar · View at Scopus
  19. L.-P. Zhang, H.-J. Yu, and S.-X. Hu, “Optimal choice of parameters for particle swarm optimization,” Journal of Zhejiang University: Science, vol. 6, no. 6, pp. 528–534, 2005. View at Publisher · View at Google Scholar · View at Scopus
  20. V. N. Vapnik, Statistical Learning Theory, John Wiley & Sons, New York, NY, USA, 1998. View at MathSciNet
  21. V. Vapnik, The Nature of Statistical Learning Theory, Tsinghua University Press, Beijing, China, 2000, translated by Zhang Xuegong.
  22. V. N. Vapnik, The Nature of Statistical Learning Theory, Springer, New York, NY, USA, 1995. View at Publisher · View at Google Scholar · View at MathSciNet
  23. B. Schölkopf and A. J. Smola, Learning with Kernels: Support Vector Machines, Regularization, Optimization, and Beyond, MIT Press, Cambridge, Mass, USA, 2002.
  24. P. J. García Nieto, J. R. Alonso Fernández, V. M. González Suárez, C. Díaz Muñiz, E. García-Gonzalo, and R. Mayo Bayón, “A hybrid PSO optimized SVM-based method for predicting of the cyanotoxin content from experimental cyanobacteria concentrations in the Trasona reservoir: a case study in Northern Spain,” Applied Mathematics and Computation, vol. 260, pp. 170–187, 2015. View at Publisher · View at Google Scholar
  25. R. C. Eberhart, Y. Shi, and J. Kennedy, Swarm Intelligence, Morgan Kaufmann, San Francisco, Calif, USA, 2001.
  26. M. Clerc and J. Kennedy, “The particle swarm-explosion, stability, and convergence in a multidimensional complex space,” IEEE Transactions on Evolutionary Computation, vol. 6, no. 1, pp. 58–73, 2002. View at Publisher · View at Google Scholar · View at Scopus
  27. D. E. Goldberg, Genetic Algorithms in Search, Optimization, and Machine Learning, Addison-Wesley, New York, NY, USA, 1998.
  28. M. Mitchell, An Introduction to Genetic Algorithms, Bradford Publisher, The MIT Press, Cambridge, Mass, USA, 1998.
  29. R. Storn and K. Price, “Differential evolution—a simple and efficient heuristic for global optimization over continuous spaces,” Journal of Global Optimization, vol. 11, no. 4, pp. 341–359, 1997. View at Publisher · View at Google Scholar · View at MathSciNet
  30. M. Dorigo and T. Stützle, Ant Colony Optimization, Bradford Publisher, The MIT Press, Cambridge, Mass, USA, 2004.
  31. Y. Shi and R. Eberhart, A Modified Particle Swarm Optimizer, Anchorage, Alaska, 1998.
  32. Z. Wenfen, W. Gang, Z. Chaohui, and X. Juan, “Population size selection of particle swarm optimizer algorithm,” Computer System Application, vol. 19, no. 5, pp. 125–128, 2010. View at Google Scholar
  33. S. Liu, H. Tai, Q. Ding, D. Li, L. Xu, and Y. Wei, “A hybrid approach of support vector regression with genetic algorithm optimization for aquaculture water quality prediction,” Mathematical and Computer Modelling, vol. 58, no. 3-4, pp. 458–465, 2013. View at Publisher · View at Google Scholar · View at Scopus
  34. Ö. A. Dombayci, “The prediction of heating energy consumption in a model house by using artificial neural networks in Denizli-Turkey,” Advances in Engineering Software, vol. 41, no. 2, pp. 141–147, 2010. View at Publisher · View at Google Scholar · View at Scopus