Intelligent forecasting of residential heating demand for the District Heating System based on the monthly overall natural gas consumption

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@Article{Izadyar:2015:EB,
  author =       "Nima Izadyar and Hwai Chyuan Ong and 
                 Shahaboddin Shamshirband and Hossein Ghadamian and Chong Wen Tong",
  title =        "Intelligent forecasting of residential heating demand
                 for the District Heating System based on the monthly
                 overall natural gas consumption",
  journal =      "Energy and Buildings",
  volume =       "104",
  pages =        "208--214",
  year =         "2015",
  ISSN =         "0378-7788",
  DOI =          "doi:10.1016/j.enbuild.2015.07.006",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0378778815301225",
  abstract =     "In this study, the residential heating demand of a
                 case study (Baharestan town, Karaj) in Iran was
                 forecasted based on the monthly natural gas consumption
                 data and monthly average of the ambient temperature.
                 Three various methods containing Extreme Learning
                 Machine (ELM), artificial neural networks (ANNs) and
                 genetic programming (GP) were employed to forecast
                 residential heating demand of the case study and the
                 results of these methods were compared after validating
                 via real data. Actually, the main goal of the current
                 study is to obtain the most accurate technique among
                 these 3 common methods in this context. Validation of
                 the forecasting results reveals that the important
                 progress can be achieved in terms of accuracy by the
                 ELM method in comparison with ANN and GP. Moreover,
                 obtained results indicate that developed ELM models can
                 be used with confidence for further work on formulating
                 novel model predictive strategy for residential heating
                 demand for the DHS. The outputs reveal that the new
                 procedure can have a suitable performance in major
                 cases and can be learned more rapid compare with other
                 common learning algorithms.",
  keywords =     "genetic algorithms, genetic programming, Residential
                 natural gas demand, District Heating System (DHS),
                 Estimation, Computational models, Energy consumption",
}

Genetic Programming entries for Nima Izadyar Hwai Chyuan Ong Shahaboddin Shamshirband Hossein Ghadamian Chong Wen Tong

Citations