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Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm

Author

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  • Protić, Milan
  • Shamshirband, Shahaboddin
  • Petković, Dalibor
  • Abbasi, Almas
  • Mat Kiah, Miss Laiha
  • Unar, Jawed Akhtar
  • Živković, Ljiljana
  • Raos, Miomir

Abstract

District heating systems are important utility systems. If these systems are properly managed, they can ensure economic and environmentally friendly provision of heat to connected customers. Potentials for further improvement of district heating systems' operation lie in the improvement of current control strategies. One of the options is the introduction of model predictive control. Multi-step ahead predictive models of consumers’ heat load are a starting point for creating a successful model predictive strategy. For the purpose of this article, short-term multi-step ahead predictive models of heat load of consumers connected to a district heating system were created. The models were developed using the novel method based on SVM (Support Vector Machines) coupled with a discrete wavelet transform. Nine different SVM-WAVELET predictive models for a time horizon from 1 to 24 h ahead were developed. Estimation and prediction results of the SVM-WAVELET models were compared with GP (genetic programming) and ANN (artificial neural network) models. The experimental results show that an improvement in predictive accuracy and capability of generalization can be achieved by the SVM-WAVELET approach in comparison with GP and ANN.

Suggested Citation

  • Protić, Milan & Shamshirband, Shahaboddin & Petković, Dalibor & Abbasi, Almas & Mat Kiah, Miss Laiha & Unar, Jawed Akhtar & Živković, Ljiljana & Raos, Miomir, 2015. "Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm," Energy, Elsevier, vol. 87(C), pages 343-351.
    • Handle: RePEc:eee:energy:v:87:y:2015:i:c:p:343-351
    DOI: 10.1016/j.energy.2015.04.109
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    1. Marcellino, Massimiliano & Stock, James H. & Watson, Mark W., 2006. "A comparison of direct and iterated multistep AR methods for forecasting macroeconomic time series," Journal of Econometrics, Elsevier, vol. 135(1-2), pages 499-526.
    2. Chevillon, Guillaume & Hendry, David F., 2005. "Non-parametric direct multi-step estimation for forecasting economic processes," International Journal of Forecasting, Elsevier, vol. 21(2), pages 201-218.
    3. Guillaume Chevillon, 2007. "Direct Multi‐Step Estimation And Forecasting," Journal of Economic Surveys, Wiley Blackwell, vol. 21(4), pages 746-785, September.
    4. Lund, H. & Möller, B. & Mathiesen, B.V. & Dyrelund, A., 2010. "The role of district heating in future renewable energy systems," Energy, Elsevier, vol. 35(3), pages 1381-1390.
    5. Gadd, Henrik & Werner, Sven, 2014. "Achieving low return temperatures from district heating substations," Applied Energy, Elsevier, vol. 136(C), pages 59-67.
    6. Piacentino, Antonio & Barbaro, Chiara, 2013. "A comprehensive tool for efficient design and operation of polygeneration-based energy μgrids serving a cluster of buildings. Part II: Analysis of the applicative potential," Applied Energy, Elsevier, vol. 111(C), pages 1222-1238.
    7. Xiong, Weiming & Wang, Yu & Mathiesen, Brian Vad & Lund, Henrik & Zhang, Xiliang, 2015. "Heat roadmap China: New heat strategy to reduce energy consumption towards 2030," Energy, Elsevier, vol. 81(C), pages 274-285.
    8. Reidhav, Charlotte & Werner, Sven, 2008. "Profitability of sparse district heating," Applied Energy, Elsevier, vol. 85(9), pages 867-877, September.
    9. Böttger, Diana & Götz, Mario & Theofilidi, Myrto & Bruckner, Thomas, 2015. "Control power provision with power-to-heat plants in systems with high shares of renewable energy sources – An illustrative analysis for Germany based on the use of electric boilers in district heatin," Energy, Elsevier, vol. 82(C), pages 157-167.
    10. Nilsson, Stefan Forsaeus & Reidhav, Charlotte & Lygnerud, Kristina & Werner, Sven, 2008. "Sparse district-heating in Sweden," Applied Energy, Elsevier, vol. 85(7), pages 555-564, July.
    11. Piacentino, Antonio & Barbaro, Chiara & Cardona, Fabio & Gallea, Roberto & Cardona, Ennio, 2013. "A comprehensive tool for efficient design and operation of polygeneration-based energy μgrids serving a cluster of buildings. Part I: Description of the method," Applied Energy, Elsevier, vol. 111(C), pages 1204-1221.
    12. Armstrong, J. Scott & Collopy, Fred, 1992. "Error measures for generalizing about forecasting methods: Empirical comparisons," International Journal of Forecasting, Elsevier, vol. 8(1), pages 69-80, June.
    13. Amiri, S. & Moshfegh, B., 2010. "Possibilities and consequences of deregulation of the European electricity market for connection of heat sparse areas to district heating systems," Applied Energy, Elsevier, vol. 87(7), pages 2401-2410, July.
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