139
Views
1
CrossRef citations to date
0
Altmetric
Research Article

Simulation study of district heating control based on load forecasting

, , , &
Received 08 Jul 2021, Accepted 13 Dec 2021, Published online: 30 Dec 2021

References

  • Aoun, N., R. Baviere, M. Vallee, A. Aurousseau, and G. Sandou. 2019. Modelling and flexible predictive control of buildings space-heating demand in district heating systems. Energy 188 (Dec.1):116042.1–116042.17. doi:https://doi.org/10.1016/j.energy.2019.116042.
  • Cholewa, T., A. Siuta-Olcha, A. Smolarz, P. Muryjas, P. Wolszczak, Ł. Guz, and C. A. Balaras. 2021b. On the short term forecasting of heat power for heating of building. Journal of Cleaner Production 307:127232. doi:https://doi.org/10.1016/j.jclepro.2021.127232.
  • Cholewa, T., A. Siuta-Olcha, A. Smolarz, P. Muryjas, P. Wolszczak, R. Anasiewicz, and C. A. Balaras. 2021a. A simple building energy model in form of an equivalent outdoor temperature. Energy and Buildings 236:110766. doi:https://doi.org/10.1016/j.enbuild.2021.110766.
  • Cs, A., B. Aga, and B. Mma. 2020. Development, analysis and application of a predictive controller to a small-scale district heating system - sciencedirect. Applied Thermal Engineering 165: 114558.
  • Ding, S., J. H. Yang, W. Lu, and Z. P. Duan. 2017. Research on central heating system control strategy based on genetic algorithm. 2017 International Conference on Advances in Materials, Machinery, Electronics (AMME), Hunan, China.
  • Embaye, M., R. K. Al-Dadah, and S. Mahmoud. 2014. Effect of flow pulsation on energy consumption of a radiator in a centrally heated building. International Journal of Low-Carbon Technologies ctu024. doi:https://doi.org/10.1093/ijlct/ctu024.
  • Fang, T., and R. Lahdelma. 2015. Genetic optimization of multi-plant heat production in district heating networks. Applied Energy 159:610–19. doi:https://doi.org/10.1016/j.apenergy.2015.09.027.
  • Lazarevic, S., V. Congradac, A. Andjelkovic, M. Kljajic, and Z. Kanovic. 2019b. District heating substation elements modeling for the development of the real-time model. Thermal Science 23 (3 Part B):31–31. doi:https://doi.org/10.2298/TSCI181226031L.
  • Lazarevic, S., V. Congradac, A. S. Andelkovic, D. Capko, and Z. Kanovic. 2019a. A novel approach to real-time modelling of the district heating substation system using labview. Journal of Cleaner Production 217 (APR.20):360–70. doi:https://doi.org/10.1016/j.jclepro.2019.01.279.
  • Li, G. Y., and L. J. Yang. 2018. Neural. Fuzzy. Predictive control and its MATLAB implementation, 4th ed. ( [M]) Beijing, China: Publishing House of Electronics Industry.
  • Li, L., and M. Zaheeruddin. 2004. A control strategy for energy optimal operation of a direct district heating system. International Journal of Energy Research 28 (7):597–612. doi:https://doi.org/10.1002/er.987.
  • Li, Z., Y. Kang, H. Wang, M. Li, and D. Fang. 2021. Real-time prediction of primary flow by MPC method in heating system. Journal of System Simulation 33 (1):180–88.
  • Liu, A., and L. Fu. 2021. China statistical yearbook, 817. Beijing, China: China statistics press.
  • Meiling, Z. (2020) Research on optimization model and algorithm of central heating system. Mater thesis: Zhejiang University of Technology.
  • Ouaret, A., L. Hocine, B. Mendil, and H. Guéguen. 2020. Supervisory control of building heating system with insulation changes using three architectures of neural networks. Journal of the Franklin Institute 357 (18):13362–85. doi:https://doi.org/10.1016/j.jfranklin.2020.09.027.
  • Qi, W. G., L. Chen, and X. L. Zhu. 2007. Study on least square modeling of heat engineering object based on typical signal response. Journal of Harbin Institute of Technology 14 (1):1–4.
  • Salo, S., A. Hast, J. Jokisalo, R. Kosonen, S. Syri, J. Hirvonen, and K. Martin. 2019. The impact of optimal demand response control and thermal energy storage on a district heating system. Energies 12 (9):1678. doi:https://doi.org/10.3390/en12091678.
  • Sun, T., Z. Nie, and L. Rong. 2011. Application on fuzzy PID technology for central heating. International Conference on Computer Science & Network Technology, Harbin, China. IEEE.
  • Tan, K. K., S. Huang, and R. Ferdous. 2002. Robust self-tuning PID controller for nonlinear systems. Journal of Process Control. Vol. 12, 753–761.
  • Tian, Y. Z., Q. Y. Yan, and B. W. Z. 2008. Heat Supply Engineering. Beijing, China: China Machine Press.
  • Ulpiani, G., M. Borgognoni, A. Romagnoli, and C. D. Perna. 2016. Comparing the performance of on/off, pid and fuzzy controllers applied to the heating system of an energy-efficient building. Energy and Buildings 116:1–17. doi:https://doi.org/10.1016/j.enbuild.2015.12.027.
  • Wang, H., F. Tu, G. Feng, and X. Ao. 2018a. Central heating system constrained control with input delay based on neural networks. Mathematical Problems in Engineering 2018:1–12.
  • Wang, Y., J. M. Kuckelkorn, D. Li, and J. Du. 2018b. A novel coupling control with decision-maker and pid controller for minimizing heating energy consumption and ensuring indoor environmental quality. Journal of Building Physics 43 (2):174425911879258.
  • Wang, Y., S. You, X. Zheng, and H. Zhang. 2017. Accurate model reduction and control of radiator for performance enhancement of room heating system. Energy and Buildings 138 (MAR):415–31. doi:https://doi.org/10.1016/j.enbuild.2016.12.034.
  • Xu, and D. Cheng. 2014. Research on the technology of intelligent control terminal personalized heating heating system. Advanced Materials Research 860-863:720–23.
  • Yabanova, İ., and A. Keçebaş. 2013. Development of ann model for geothermal district heating system and a novel pid-based control strategy. Applied Thermal Engineering 51 (1–2):908–16. doi:https://doi.org/10.1016/j.applthermaleng.2012.10.044.
  • Zeng, J., Q. Xu, Y. Ning, and X. Zhang. 2019. Pipe network optimization in district cooling/heating system: a review. 2019 International Conference on Robots & Intelligent System (ICRIS), Hankou, China.
  • Zhang, B. Y., and J. Xiao. 2011. Pi-type generalized predictive control based on ga parameter optimization and tuning. Science Technology and Engineering 11(02): 367–370.
  • Zhang, L., Y. Li, H. Zhang, X. Xu, Z. Yang, and W. Xu. 2021. A review of the potential of district heating system in northern China. Applied Thermal Engineering 188 (16):116605. doi:https://doi.org/10.1016/j.applthermaleng.2021.116605.
  • Zhang, W. W., F. Y. Yu, Y. H. Luo, and J. Y. Zhang. 2007. A fast modeling for chain grate stoker based on step response curve. Industrial Boiler.
  • Zhang, Y., J. Xia, H. Fang, Y. Jiang, and Z. Liang. 2020. Field tests on the operational energy consumption of Chinese district heating systems and evaluation of typical associated problems. Energy and Buildings 224:110269. doi:https://doi.org/10.1016/j.enbuild.2020.110269.
  • Zhao, B. W., W. Li, and Y. Jin. 2021. Heat load prediction based on PSO-LSSVM. Building Energy Efficiency 49 (6):46–49+78.

Reprints and Corporate Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

To request a reprint or corporate permissions for this article, please click on the relevant link below:

Academic Permissions

Please note: Selecting permissions does not provide access to the full text of the article, please see our help page How do I view content?

Obtain permissions instantly via Rightslink by clicking on the button below:

If you are unable to obtain permissions via Rightslink, please complete and submit this Permissions form. For more information, please visit our Permissions help page.