Extreme learning machine for prediction of heat load in district heating systems

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@Article{Sajjadi:2016:EB,
  author =       "Shahin Sajjadi and Shahaboddin Shamshirband and 
                 Meysam Alizamir and Por Lip Yee and Zulkefli Mansor and 
                 Azizah Abdul Manaf and Torki A. Altameem and 
                 Ali Mostafaeipour",
  title =        "Extreme learning machine for prediction of heat load
                 in district heating systems",
  journal =      "Energy and Buildings",
  volume =       "122",
  pages =        "222--227",
  year =         "2016",
  ISSN =         "0378-7788",
  DOI =          "doi:10.1016/j.enbuild.2016.04.021",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0378778816302766",
  abstract =     "District heating systems are important utility
                 systems. If these systems are properly managed, they
                 can ensure economic and environmental friendly
                 provision of heat to connected customers. Potentials
                 for further improvement of district heating systems'
                 operation lie in improvement of present control
                 strategies. One of the options is introduction of model
                 predictive control. Multistep ahead predictive models
                 of consumers' heat load are starting point for creating
                 successful model predictive strategy. In this article,
                 short-term, multistep ahead predictive models of heat
                 load of consumer attached to district heating system
                 were created. Models were developed using the novel
                 method based on Extreme Learning Machine (ELM). Nine
                 different ELM predictive models, for time horizon from
                 1 to 24 h ahead, were developed. Estimation and
                 prediction results of ELM models were compared with
                 genetic programming (GP) and artificial neural networks
                 (ANNs) models. The experimental results show that an
                 improvement in predictive accuracy and capability of
                 generalization can be achieved by the ELM approach in
                 comparison with GP and ANN. Moreover, achieved results
                 indicate that developed ELM models can be used with
                 confidence for further work on formulating novel model
                 predictive strategy in district heating systems. The
                 experimental results show that the new algorithm can
                 produce good generalization performance in most cases
                 and can learn thousands of times faster than
                 conventional popular learning algorithms.",
  keywords =     "genetic algorithms, genetic programming, District
                 heating systems, Heat load, Estimation, Prediction,
                 Extreme Learning Machine (ELM)",
}

Genetic Programming entries for Shahin Sajjadi Shahaboddin Shamshirband Meysam Alizamir Por Lip Yee Zulkefli Mansor Azizah Abdul Manaf Torki A Altameem Ali Mostafaeipour

Citations