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Energy Sources, Part A: Recovery, Utilization, and Environmental Effects Volume 28, 2006 - Issue 15
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Original Articles

Genetic Algorithm (GA) Approaches for the Transport Energy Demand Estimation: Model Development and Application

Olcay Ersel Canyurt Mechanical Engineering Department, Faculty of Engineering, Pamukkale University, Denizli, Turkey
,
Harun Kemal Ozturk Mechanical Engineering Department, Faculty of Engineering, Pamukkale University, Denizli, Turkey
,
Arif Hepbasli Mechanical Engineering Department, Faculty of Engineering, Ege University, Izmir, TurkeyView further author information
&
Zafer Utlu Gulhane Military Medicine Academy, Education and Teaching Department, İstanbul, Turkey
Pages 1405-1413 | Published online: 23 Feb 2007

Citations (15)

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Ersin Korkmaz & Ali Payıdar Akgüngör. (2018) Flower pollination algorithm approach for the transportation energy demand estimation in Turkey: model development and application. Energy Sources, Part B: Economics, Planning, and Policy 13:11-12, pages 429-447.
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Articles from other publishers (14)

Ersin Korkmaz. (2023) Energy demand estimation in Turkey according to modes of transportation: Bezier search differential evolution and black widow optimization algorithms-based model development and application. Neural Computing and Applications 35:9, pages 7125-7146.
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Mehmet Beşkirli. (2022) A novel Invasive Weed Optimization with levy flight for optimization problems: The case of forecasting energy demand. Energy Reports 8, pages 1102-1111.
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Mohammad Ali Sahraei, Hakan Duman, Muhammed Yasin Çodur & Ecevit Eyduran. (2021) Prediction of transportation energy demand: Multivariate Adaptive Regression Splines. Energy 224, pages 120090.
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Mert Sinan Turgut, Uğur Eliiyi, Oguz Emrah Turgut, Erdinç Öner & Deniz Türsel Eliiyi. (2021) Artificial Intelligence Approaches to Estimate the Transport Energy Demand in Turkey. Arabian Journal for Science and Engineering 46:3, pages 2443-2476.
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Ajay Raghavan, Paarth Maan & Ajitha K. B. Shenoy. (2020) Optimization of Day-Ahead Energy Storage System Scheduling in Microgrid Using Genetic Algorithm and Particle Swarm Optimization. IEEE Access 8, pages 173068-173078.
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F. Enda Tolon, Arif Karabuga, Mert Tolon & Zafer Utlu. (2019) Evaluation of Thermodynamic Analysis of Solar Energy Systems Integrated into Sustainable Buildings with Artificial Neural Network: A Case Study. Procedia Computer Science 158, pages 91-98.
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Geer Teng, Jin Xiao, Yue He, Tingting Zheng & Changzheng He. (2017) Use of group method of data handling for transport energy demand modeling. Energy Science & Engineering 5:5, pages 302-317.
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Mehmet Bilgili, Besir Sahin, Abdulkadir Yasar & Erdogan Simsek. (2012) Electric energy demands of Turkey in residential and industrial sectors. Renewable and Sustainable Energy Reviews 16:1, pages 404-414.
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N.R. Badurally Adam, M.K. Elahee & M.Z. Dauhoo. (2011) Forecasting of peak electricity demand in Mauritius using the non-homogeneous Gompertz diffusion process. Energy 36:12, pages 6763-6769.
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M.H. Amjadi, H. Nezamabadi-pour & M.M. Farsangi. (2010) Estimation of electricity demand of Iran using two heuristic algorithms. Energy Conversion and Management 51:3, pages 493-497.
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Kadir Kavaklioglu, Halim Ceylan, Harun Kemal Ozturk & Olcay Ersel Canyurt. (2009) Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks. Energy Conversion and Management 50:11, pages 2719-2727.
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Halim Ceylan & Huseyin Ceylan. 2009. Music-Inspired Harmony Search Algorithm. Music-Inspired Harmony Search Algorithm 163 172 .
Huseyin Ceylan, Halim Ceylan, Soner Haldenbilen & Ozgur Baskan. (2008) Transport energy modeling with meta-heuristic harmony search algorithm, an application to Turkey. Energy Policy 36:7, pages 2527-2535.
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M. Duran Toksarı. (2007) Ant colony optimization approach to estimate energy demand of Turkey. Energy Policy 35:8, pages 3984-3990.
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