Table of Contents Author Guidelines Submit a Manuscript
Mathematical Problems in Engineering
Volume 2018, Article ID 2908608, 14 pages
https://doi.org/10.1155/2018/2908608
Research Article

Neural Network Based Central Heating System Load Prediction and Constrained Control

1School of Municipal and Environmental Engineering, Shenyang Jianzhu University, Shenyang, Liaoning 110168, China
2Department of Electrical and Computer Engineering, National University of Singapore, Singapore 117576
3The Third Design Division, China Northeast Architectural Design and Research Institute Co., Ltd., Shenyang, Liaoning 110003, China

Correspondence should be addressed to Hongwei Wang; nc.ude.uzjs@whw_jh

Received 11 July 2017; Revised 7 January 2018; Accepted 10 January 2018; Published 7 February 2018

Academic Editor: Mauro Gaggero

Copyright © 2018 Hongwei 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.

Linked References

  1. M. Wang and Q. Tian, “Dynamic heat supply prediction using support vector regression optimized by particle swarm optimization algorithm,” Mathematical Problems in Engineering, vol. 2016, Article ID 3968324, 10 pages, 2016. View at Publisher · View at Google Scholar · View at Scopus
  2. M. Aydinalp, V. I. Ugursal, and A. S. Fung, “Modeling of the space and domestic hot-water heating energy-consumption in the residential sector using neural networks,” Applied Energy, vol. 79, no. 2, pp. 159–178, 2004. View at Publisher · View at Google Scholar · View at Scopus
  3. 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
  4. H. A. Nielsen and H. Madsen, “Modelling the heat consumption in district heating systems using a grey-box approach,” Energy and Buildings, vol. 38, no. 1, pp. 63–71, 2006. View at Publisher · View at Google Scholar · View at Scopus
  5. P. Pinson, T. S. Nielsen, H. A. Nielsen, N. K. Poulsen, and H. Madsen, “Temperature prediction at critical points in district heating systems,” European Journal of Operational Research, vol. 194, no. 1, pp. 163–176, 2009. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  6. A. Kusiak, M. Li, and Z. Zhang, “A data-driven approach for steam load prediction in buildings,” Applied Energy, vol. 87, no. 3, pp. 925–933, 2010. View at Publisher · View at Google Scholar · View at Scopus
  7. L. Li and M. Zaheeruddin, “A control strategy for energy optimal operation of a direct district heating system,” International Journal of Energy Research, vol. 28, no. 7, pp. 597–612, 2004. View at Publisher · View at Google Scholar · View at Scopus
  8. A. Ljubenko, A. Poredoš, and M. Zager, “Effects of hot-water-pipeline renovation in a district heating system,” Strojniski Vestnik: Journal of Mechanical Engineering, vol. 57, no. 11, pp. 834–842, 2011. View at Publisher · View at Google Scholar · View at Scopus
  9. D. Liu, X. Yang, D. Wang, and Q. Wei, “Reinforcement-learning-based robust controller design for continuous-time uncertain nonlinear systems subject to input constraints,” IEEE Transactions on Cybernetics, vol. 45, no. 7, pp. 1372–1385, 2015. View at Publisher · View at Google Scholar · View at Scopus
  10. X. Yang, D. Liu, H. Ma, and Y. Xu, “Online approximate solution of HJI equation for unknown constrained-input nonlinear continuous-time systems,” Information Sciences, vol. 328, pp. 435–454, 2016. View at Publisher · View at Google Scholar · View at Scopus
  11. X.-F. Meng, Y. Wang, and M.-L. Lv, “Adaptive NN control for multisteering plane aircraft with dead zone or backlash input nonlinearity,” Mathematical Problems in Engineering, vol. 2017, Article ID 4684303, 2017. View at Publisher · View at Google Scholar · View at Scopus
  12. M. Chen, S. S. Ge, and B. Ren, “Adaptive tracking control of uncertain MIMO nonlinear systems with input constraints,” Automatica, vol. 47, no. 3, pp. 452–465, 2011. View at Publisher · View at Google Scholar · View at Scopus
  13. F. Tu, S. S. Ge, Y. S. Choo, and C. C. Hang, “Adaptive dynamic positioning control for accommodation vessels with multiple constraints,” IET Control Theory & Applications, vol. 11, no. 3, pp. 329–340, 2017. View at Publisher · View at Google Scholar · View at MathSciNet
  14. H. Modares, F. L. Lewis, and M.-B. Naghibi-Sistani, “Integral reinforcement learning and experience replay for adaptive optimal control of partially-unknown constrained-input continuous-time systems,” Automatica, vol. 50, no. 1, pp. 193–202, 2014. View at Publisher · View at Google Scholar · View at Scopus
  15. G. Wen, S. S. Ge, F. Tu, and Y. S. Choo, “Artificial potential-based adaptive h∞ synchronized tracking control for accommodation vessel,” IEEE Transactions on Industrial Electronics, vol. 64, no. 7, pp. 5640–5647, 2017. View at Publisher · View at Google Scholar · View at Scopus
  16. C. P. Bechlioulis and G. A. Rovithakis, “Prescribed performance adaptive control for multi-input multi-output affine in the control nonlinear systems,” Institute of Electrical and Electronics Engineers Transactions on Automatic Control, vol. 55, no. 5, pp. 1220–1226, 2010. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  17. W. Xiang, N. Li, and Y. Sun, “Fuzzy adaptive prescribed performance control for a class of uncertain nonlinear systems with unknown dead-zone inputs,” Mathematical Problems in Engineering, vol. 2017, Article ID 4386515, 10 pages, 2017. View at Publisher · View at Google Scholar · View at Scopus
  18. D. Q. Mayne, J. B. Rawlings, C. V. Rao, and P. O. M. Scokaert, “Constrained model predictive control: stability and optimality,” Automatica, vol. 36, no. 6, pp. 789–814, 2000. View at Publisher · View at Google Scholar · View at Scopus
  19. E. Gilbert and I. Kolmanovsky, “Nonlinear tracking control in the presence of state and control constraints: A generalized reference governor,” Automatica, vol. 38, no. 12, pp. 2063–2073, 2002. View at Publisher · View at Google Scholar · View at Scopus
  20. K. P. Tee, B. Ren, and S. S. Ge, “Control of nonlinear systems with time-varying output constraints,” Automatica, vol. 47, no. 11, pp. 2511–2516, 2011. View at Publisher · View at Google Scholar · View at Scopus
  21. Y.-J. Liu, J. Li, S. Tong, and C. L. Chen, “Neural network control-based adaptive learning design for nonlinear systems with full-state constraints,” IEEE Transactions on Neural Networks and Learning Systems, vol. 27, no. 7, pp. 1562–1571, 2016. View at Publisher · View at Google Scholar · View at MathSciNet · View at Scopus
  22. P. He, G. Sun, and F. Wang, Heating Engineering, China Architecture and Building Press, Beijing, China, 1993. View at Publisher · View at Google Scholar
  23. V. Kurkova, “Kolmogorov’s theorem is relevant,” Neural Computation, vol. 3, no. 4, pp. 617–622, 1991. View at Publisher · View at Google Scholar
  24. 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
  25. S. S. Ge, T. H. Lee, and C. J. Harris, Adaptive neural network control of robotic manipulators, vol. 19, World Scientific, 1998.
  26. L. C. W. QI and X. Zhu, “Study on least square modeling of heat engineering object based on typical signal response,” Journal of Harbin Institute of Technology, vol. 14, no. 1, pp. 1–4, 2007. View at Google Scholar
  27. M. Krstic, “On compensating long actuator delays in nonlinear control,” in Proceedings of the 2008 American Control Conference, ACC, pp. 2921–2926, USA, June 2008. View at Publisher · View at Google Scholar · View at Scopus
  28. N. Sharma, S. Bhasin, Q. Wang, and W. E. Dixon, “Predictor-based control for an uncertain EulerLagrange system with input delay,” Automatica, vol. 47, no. 11, pp. 2332–2342, 2011. View at Publisher · View at Google Scholar · View at Scopus
  29. N. Fischer, A. Dani, N. Sharma, and W. E. Dixon, “Saturated control of an uncertain nonlinear system with input delay,” Automatica, vol. 49, no. 6, pp. 1741–1747, 2013. View at Publisher · View at Google Scholar · View at Scopus
  30. K. P. Tee, S. S. Ge, and E. H. Tay, “Barrier Lyapunov functions for the control of output-constrained nonlinear systems,” Automatica, vol. 45, no. 4, pp. 918–927, 2009. View at Publisher · View at Google Scholar · View at Scopus
  31. Z. Zhao, W. He, and S. S. Ge, “Adaptive neural network control of a fully actuated marine surface vessel with multiple output constraints,” IEEE Transactions on Control Systems Technology, vol. 22, no. 4, pp. 1536–1543, 2014. View at Publisher · View at Google Scholar · View at Scopus
  32. S. S. Ge and C. Wang, “Direct adaptive NN control of a class of nonlinear systems,” IEEE Transactions on Neural Networks and Learning Systems, vol. 13, no. 1, pp. 214–221, 2002. View at Publisher · View at Google Scholar · View at Scopus
  33. K. P. Tee and S. S. Ge, “Control of fully actuated ocean surface vessels using a class of feedforward approximators,” IEEE Transactions on Control Systems Technology, vol. 14, no. 4, pp. 750–756, 2006. View at Publisher · View at Google Scholar · View at Scopus
  34. H. B. Demuth, M. H. Beale, O. De Jess, and M. T. Hagan, Neural Network Design, Martin Hagan, 2014.