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

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@Article{Protic:2015:Energy,
  author =       "Milan Protic and Shahaboddin Shamshirband and 
                 Dalibor Petkovic and Almas Abbasi and Miss Laiha Mat Kiah and 
                 Jawed Akhtar Unar and Ljiljana Zivkovic and 
                 Miomir Raos",
  title =        "Forecasting of consumers heat load in district heating
                 systems using the support vector machine with a
                 discrete wavelet transform algorithm",
  journal =      "Energy",
  volume =       "87",
  pages =        "343--351",
  year =         "2015",
  ISSN =         "0360-5442",
  DOI =          "doi:10.1016/j.energy.2015.04.109",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0360544215005976",
  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.",
  keywords =     "genetic algorithms, genetic programming, District
                 heating systems, Heat load, Estimation, Prediction,
                 Support vector machine, Wavelet transform",
  notes =        "University of Nis, Faculty of Occupational Safety,
                 Carnojevi, Serbia",
}

Genetic Programming entries for Milan Protic Shahaboddin Shamshirband Dalibor Petkovic Almas Abbasi Miss Laiha Mat Kiah Jawed Akhtar Unar Ljiljana Zivkovic Miomir Raos

Citations