Appraisal of the support vector machine to forecast residential heating demand for the District Heating System based on the monthly overall natural gas consumption

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@Article{Izadyar:2015:Energy,
  author =       "Nima Izadyar and Hossein Ghadamian and 
                 Hwai Chyuan Ong and Zeinab moghadam and Chong Wen Tong and 
                 Shahaboddin Shamshirband",
  title =        "Appraisal of the support vector machine to forecast
                 residential heating demand for the District Heating
                 System based on the monthly overall natural gas
                 consumption",
  journal =      "Energy",
  volume =       "93, Part 2",
  pages =        "1558--1567",
  year =         "2015",
  ISSN =         "0360-5442",
  DOI =          "doi:10.1016/j.energy.2015.10.015",
  URL =          "http://www.sciencedirect.com/science/article/pii/S0360544215013791",
  abstract =     "DHS (District Heating System) is one of the most
                 efficient technologies which has been used to meet
                 residential thermal demand. In this study, the most
                 accurate forecasting of the residential heating demand
                 is investigated via soft computing method. The
                 objective of this study is to obtain the most accurate
                 prediction of the residential heating consumption to
                 employ forecasting result for designing optimum DHS
                 system as a possible substitute of a pipeline natural
                 gas in BAHARESTAN Town. For this purpose, three Support
                 Vector Machine (SVM) models namely SVM coupled with the
                 discrete wavelet transform (SVM-Wavelet), the firefly
                 algorithm (SVM-FFA) and using the radial basis function
                 (SVM-RBF) were analysed. The estimation and prediction
                 results of these models were compared with two other
                 soft computing methods (ANN (Artificial Neural Network)
                 and GP (Genetic programming)) by using three
                 statistical indicators i.e. RMSE (root means square
                 error), coefficient of determination (R2) and Pearson
                 coefficient (r). Based on the experimental outputs, the
                 SVM-Wavelet method can lead to slightly accurate
                 forecasting of the monthly overall natural gas
                 demand.",
  keywords =     "genetic algorithms, genetic programming, Residential
                 natural gas demand, DHS (District heating system),
                 Estimation, Wavelet and firefly algorithms (FFAs), SVM
                 (Support vector machine)",
}

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

Citations