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,
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author = "Nima Izadyar and Hossein Ghadamian and
Hwai Chyuan Ong and Zeinab moghadam and Chong Wen Tong and
Shahaboddin Shamshirband",
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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",
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journal = "Energy",
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volume = "93, Part 2",
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pages = "1558--1567",
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year = "2015",
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ISSN = "0360-5442",
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DOI = "doi:10.1016/j.energy.2015.10.015",
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URL = "http://www.sciencedirect.com/science/article/pii/S0360544215013791",
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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.",
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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