Forecasting of consumers heat load in district heating systems using the support vector machine with a discrete wavelet transform algorithm
Created by W.Langdon from
gp-bibliography.bib Revision:1.8028
- @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",
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title = "Forecasting of consumers heat load in district heating
systems using the support vector machine with a
discrete wavelet transform algorithm",
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journal = "Energy",
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volume = "87",
-
pages = "343--351",
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year = "2015",
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keywords = "genetic algorithms, genetic programming, District
heating systems, Heat load, Estimation, Prediction,
Support vector machine, SVM, ANN, Wavelet transform",
-
ISSN = "0360-5442",
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DOI = "doi:10.1016/j.energy.2015.04.109",
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URL = "http://www.sciencedirect.com/science/article/pii/S0360544215005976",
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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.",
-
notes = "University of Nis, Faculty of Occupational Safety,
Carnojevi, Serbia",
- }
Genetic Programming entries for
Milan Protic
Shahaboddin Shamshirband
Dalibor Petkovic
Almas Abbasi
Prof Dr Miss Laiha Binti Mat Kiah
Jawed Akhtar Unar
Ljiljana Zivkovic
Miomir Raos
Citations