Development of future energy scenarios with intelligent algorithms: Case of hydro in Turkey

Energy production is considered as one of the key indicators for economic development. It is vital to improve the renewable energy production for global sustainability, while leveraging the national resources. This study is contributing to the demonstration of using genetic algorithms (GA) in the de...

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Bibliographic Details
Published in:Energy (Oxford) Vol. 35; no. 4; pp. 1724 - 1729
Main Authors: Cinar, Didem, Kayakutlu, Gulgun, Daim, Tugrul
Format: Journal Article
Language:English
Published: Kidlington Elsevier Ltd 01.04.2010
Elsevier
Subjects:
Applied sciences
Back-propagation
Economic data
Electric energy
Energy
Energy economics
Exact sciences and technology
General, economic and professional studies
Genetic algorithms
Methodology. Modelling
Natural energy
Neural networks
Renewable energy
Asia
Turkey
Renewable energy
Neural networks
Back-propagation
Genetic algorithms
Short term
Case study
Hydraulic power
Backpropagation algorithm
Electricity
Methodology
Genetic algorithm
Electric power production
Forecasting
ISSN:0360-5442
Online Access:Get full text
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Summary:Energy production is considered as one of the key indicators for economic development. It is vital to improve the renewable energy production for global sustainability, while leveraging the national resources. This study is contributing to the demonstration of using genetic algorithms (GA) in the development of future energy scenarios as well as to the strategic energy studies in Turkey. The forecasting model developed in this study uses forward feeding back-propagation (BP) method improved by GA. The proposed model is applied in the Turkish case. The test errors are shown to emphasize the positive difference between the proposed model and the classical BP model. The results highlight that there is strong evidence indicating that the government should reconsider their current energy strategies.
Bibliography:ObjectType-Article-2
SourceType-Scholarly Journals-1
ObjectType-Feature-1
content type line 23
ISSN:0360-5442
DOI:10.1016/j.energy.2009.12.025