International Scientific Journal

Thermal Science - Online First

online first only

Prediction of heating load fluctuation based on fuzzy information granulation and support vector machine

District heating systems are an important part of the future smart energy system and are seen as a tool to achieve energy efficiency goals in the EU. In order to achieve the real sense of heating on demand, based on historical heating load data, first of all, the heating load time series data was dealing with fuzzy information granulation, and then the cross-validation was used to explore the advantages of the data potential. Then the support vector machine regression prediction model was used for the prediction of the granulation data, finally, the heating load of a district heating system is simulated and verified. The simulation results show that the prediction model can effectively predict the trend of heating load, and provide a theoretical basis for the prediction of district heating load.
PAPER REVISED: 2020-07-29
PAPER ACCEPTED: 2020-08-21
  1. M√ľnster, M., et al., The role of district heating in the future Danish energy system. Energy, 48(2012), pp.47-55.
  2. Werner, S., International review of district heating and cooling. Energy, 137(2017), pp. 617-631.
  3. Lund, H., et al., 4th Generation District Heating (4GDH). Energy, 68(2014), pp.1-11.
  4. Nielsen, H.H., Madsen, H., Modelling the heat consumption in district heating systems using a grey-box approach. Energy & Buildings, 38(2005), 1, pp.63-71.
  5. Popescu, D., et al., Simulation models for the analysis of space heat consumption of buildings. Energy, 34(2009), 10, pp.1447-1453.
  6. Fu, X., et al., Thermal Load Prediction Considering Solar Radiation and Weather. Energy Procedia, 103(2016), pp.3-8.
  7. Sajjadi, S., et al., Extreme learning machine for prediction of heat load in district heating systems. Energy & Buildings, 122(2016), pp.222-227.
  8. Liu P.F., et al., Prediction of district heating load based on grey neural network model. DEStech Transactions on Computer Science and Engineering, 49(2019), 05, pp.124-128.
  9. Mehmood, M.U., et al., A review of the applications of artificial intelligence and big data to buildings for energy-efficiency and a comfortable indoor living environment. Energy & Buildings,2019, 202.
  10. Jain, R.K., et al., Forecasting energy consumption of multi-family residential buildings using support vector regression: Investigating the impact of temporal and spatial monitoring granularity on performance accuracy. Applied Energy, 123(2014), pp.168-178.
  11. Al-Shammari, E.T., et al., Prediction of heat load in district heating systems by Support Vector Machine with Firefly searching algorithm. Energy, 95(2016), pp.266-273.
  12. Cristianini, N., Shawe-taylor, J., An introduction to support vector machines and other Kernel-based learning methods. New York: Cambridge University Press,2000, pp.93-124.
  13. Yan, X., The Research on the Prediction of the Network Traffic Based on the Improved Psosvm Algorithm. Chemical Engineering Transactions (CET Journal), 46(2015).
  14. Kouziokas, G.N., A new W-SVM kernel combining PSO-neural network transformed vector and Bayesian optimized SVM in GDP forecasting. Engineering Applications of Artificial Intelligence, 92(2020).
  15. Sachinkumar, V., Patil N. B., Novel LBP based texture descriptor for rotation, illumination and scale invariance for image texture analysis and classification using multi-kernel SVM. Multimedia Tools and Applications, 79(2020), 15, pp. 9935-9955.
  16. He, Y.Y., et al., Uncertainty Forecasting for Streamflow based on Support Vector Regression Method with Fuzzy Information Granulation. Energy Procedia, 158(2019), pp.6189-6194.
  17. Zadel, L.A., Towards a theory of fuzzy information granulation and its centrality in human reasoning and fuzzy logic. Fuzzy Sets and System, 90(1997), 2, pp.111-127.
  18. Wang, H., et al., Based on the fuzzy information granulation and least squares support vector machine (SVM) range of combination forecast model of wind power fluctuations. Journal of electrical engineering technology, 29(2014), 12, pp.218-224.
  19. Ulieru, M., Pedrycz, W., Knowledge-Based Clustering: From Data to Information Granules. Information Processing and Management,42(2005),1, pp.321-322.
  20. Bargiela, A., Pedrycz, W., Granular computing: an introduction. Dodrecht: Kluwer Academic Publishers, 2003.
  21. Chapelle, O., et al., Choosing multiple parameters for support vector machines. Machine Learning, 46(2002), 1-3, pp.131-159.
  22. Cortes, C., Vapnik, V., Support-vector networks. Machine Learning,20(1995), 3, pp.273-297.
  23. Guo, J.H., et al., Short-term traffic flow prediction using fuzzy information granulation approach under different time intervals. IET Intelligent Transport Systems,12(2018), 2, pp.143-150.
  24. Xiao, B., et al., Space load prediction method using fuzzy information granulation and support vector machine. Power grid technology, pp.1-10.
  25. Tian, S.X., et al., Application of support vector machine based on the optimization algorithm of flock of pigeons in the prediction of total power demand. Power automation equipment, (2020), 05, pp.1-4.
  26. Shuhan, L.U., Si-jing, Y.E., Using an image segmentation and support vector machine method for identifying two locust species and instars. Journal of Integrative Agriculture,19(2020),05, pp.1301-1313.
  27. Li, X.X., Zhang, X.S., Fuzzy information granulation support vector regression model based on wavelet transform and its application. Quantitative economy research, 10(2019),04, pp.127-143.