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Determination with Linear Form of Turkey's Energy Demand Forecasting by the Tree Seed Algorithm and the Modified Tree Seed AlgorithmBESKIRLI, A. , TEMURTAS, H. , OZDEMIR, D.
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algorithms, demand forecasting, energy optimization, heuristic algorithms
energy(45), demand(19), turkey(17), algorithm(17), optimization(13), systems(8), artificial(8), forecasting(7), applications(7), neural(6)
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About this article
Date of Publication: 2020-05-31
Volume 20, Issue 2, Year 2020, On page(s): 27 - 34
ISSN: 1582-7445, e-ISSN: 1844-7600
Digital Object Identifier: 10.4316/AECE.2020.02004
Web of Science Accession Number: 000537943500004
SCOPUS ID: 85087464201
Energy plays an important role in every stage of human life in different forms and variations. Along with the developments in economic, social and industrial fields, the amount of energy that countries need is increasing day by day. Therefore, it is significant to estimate the energy demand for a country's economic activities accurately. In this study, the energy demand forecast (EDF) application optimization problem of Turkey, one of the real-world optimization problems, was performed by MTSA (Modified Tree Seed Algorithm) and TSA (Tree Seed Algorithm) methods. From 1979 to 2005, gross domestic product (GDP), population, export and import values were used as parameter data. Thus, in the presence of three different possible scenarios, Turkey's energy demand from 2006 to 2025, which was estimated by MTSA and TSA methods. To demonstrate the success of MTSA and TSA in the problem of energy demand forecasting (EDF), they are compared with Ant Colony Algorithm (ACO), Particle Swarm Optimization (PSO), Bat Algorithm (BA), Differential Evolution Algorithm (DEA) and Artificial Algae Algorithm (AAA) methods which are in the literature. According to the results of the analysis, it was observed that the MTSA method was a successful estimation tool for energy demand.
|References|||||Cited By «-- Click to see who has cited this paper|
| H. Ceylan and H. K. Ozturk, "Estimating energy demand of Turkey based on economic indicators using a genetic algorithm approach," Energy Conversion and Management, vol. 45, no. 15, pp. 2525-2537, 2004, |
[CrossRef] [SCOPUS Times Cited 158]
 H. Tatli and K. Besir, "The Place of Turkey in the OECD Countries in the Context of Energy Consumption and Energy Prices," vol. 8, no. 15, pp. 353-376, 2018,
 G. Gunes and E. Aslan, "Use of renewable energy sources and its effects to sustainable tourism - Turkey Example," in Dogu Karadeniz Bolgesi Surdurulebilir Turizm Kongresi, pp. 221-234: Gumushane Universitesi Yayinlari-31, Gumushane/Turkey, 2015.
 A. Sozen, E. Arcaklioglu, and M. Ozkaymak, "Modelling of Turkey's net energy consumption using artificial neural network," Int. J. Comput. Appl. Technol., vol. 22, no. 2/3, pp. 130-136, 2005,
[CrossRef] [SCOPUS Times Cited 23]
 E. Bergasse, W. Paczynski, M. Dabrowski, L. De Wulf, "The relationship between energy and socio-economic development in the Southern and Eastern Mediterranean," CASE Network Reports, no. 412, 2013.
 H. Ogurlu, "Long Term Electrical Load Forecasting of Turkey Using Mathematical Modeling," MS, Selcuk Universitesi Fen Bilimleri Enstitusu, 2011.
 M. F. Tefek, H. Uguz and M. Gucyetmez, "A new hybrid gravitational search-teaching-learning-based optimization method for energy demand estimation of Turkey," Neural Computing and Applications, vol. 31, pp. 2939-2954, 2019,
[CrossRef] [Web of Science Times Cited 6] [SCOPUS Times Cited 11]
 A. Unler, "Improvement of energy demand forecasts using swarm intelligence: The case of Turkey with projections to 2025," Energy Policy, vol. 36, no. 6, pp. 1937-1944, 2008,
[CrossRef] [Web of Science Times Cited 140] [SCOPUS Times Cited 150]
 Z. W. Geem, W. E. Roper, "Energy demand estimation of South Korea using artificial neural network," Energy Policy, vol. 37, no. 10, pp. 4049-4054, 2009,
[CrossRef] [Web of Science Times Cited 116] [SCOPUS Times Cited 125]
 L. Ekonomou, "Greek long-term energy consumption prediction using artificial neural networks," Energy, vol. 35, no. 2, pp. 512-517, 2010,
[CrossRef] [Web of Science Times Cited 235] [SCOPUS Times Cited 282]
 S. Yu, K. Zhu, "A hybrid procedure for energy demand forecasting in China," Energy, vol. 37, no. 1, pp. 396-404, 2012,
[CrossRef] [Web of Science Times Cited 43] [SCOPUS Times Cited 43]
 M. Piltan, H. Shiri, S. Ghaderi, "Energy demand forecasting in Iranian metal industry using linear and nonlinear models based on evolutionary algorithms," Energy conversion and management, vol. 58, pp. 1-9, 2012,
[CrossRef] [Web of Science Times Cited 41] [SCOPUS Times Cited 44]
 J. Sanchez-Oro, A. Duarte, S. Salcedo-Sanz, "Robust total energy demand estimation with a hybrid Variable Neighborhood Search-Extreme Learning Machine algorithm," Energy Conversion and Management, vol. 123, pp. 445-452, 2016,
[CrossRef] [Web of Science Times Cited 15] [SCOPUS Times Cited 16]
 Z. Mohamed, P. Bodger, "Forecasting electricity consumption in New Zealand using economic and demographic variables," Energy, vol. 30, no. 10, pp. 1833-1843, 2005,
[CrossRef] [Web of Science Times Cited 161] [SCOPUS Times Cited 187]
 V. Bianco, O. Manca, S. Nardini, "Electricity consumption forecasting in Italy using linear regression models," Energy, vol. 34, no. 9, pp. 1413-1421, 2009,
[CrossRef] [Web of Science Times Cited 270] [SCOPUS Times Cited 324]
 S. Yu, K. Zhu, X. Zhang, "Energy demand projection of China using a path-coefficient analysis and PSO-GA approach," Energy Conversion and Management, vol. 53, no. 1, pp.142-153, 2012,
[CrossRef] [Web of Science Times Cited 66] [SCOPUS Times Cited 77]
 S. Yu, Y. Wei, K. Wang, "A PSO-GA optimal model to estimate primary energy demand of China," Energy Policy, vol. 42, pp. 329-340, 2012,
[CrossRef] [Web of Science Times Cited 68] [SCOPUS Times Cited 81]
 E. Erdogdu, "Electricity demand analysis using cointegration and ARIMA modelling: A case study of Turkey," Energy Policy, vol. 35, no. 2, pp. 1129-1146, 2007,
[CrossRef] [Web of Science Times Cited 172] [SCOPUS Times Cited 192]
 WECTNC, World Energy Council, Energy Report-2014, Ankara, ISSN: 1301-6318 (Ankara, May). 2015.
 M. Afzalirad, M. Shafipour, "Design of an efficient genetic algorithm for a resource-constrained unrelated parallel machine scheduling problem with machine eligibility restrictions," Journal of Intelligent Manufacturing, vol. 29, no. 2, pp. 423-437, 2018,
[CrossRef] [Web of Science Times Cited 23] [SCOPUS Times Cited 32]
 A. Mucherino, O. Seref, "Modeling and solving real-life global optimization problems with meta-heuristic methods," Advances in Modeling Agricultural Systems, pp. 403-419, 2009,
[CrossRef] [SCOPUS Times Cited 7]
 I. Pence, M.S. Cesmeli, F.A. Senel, B. Cetisli, "A new unconstrained global optimization method based on clustering and parabolic approximation," Expert Systems with Applications, vol. 55, pp. 493-507, 2016,
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 9]
 A. Gaudiani, E. Luque, P. GarcÃa, M. Re, M. Naiouf and A. Giusti, "How a computational method can help to improve the quality of river flood prediction by simulation," Advances and New Trends in Environmental and Energy Informatics, pp. 337-351, 2016.
 H. Shareef, M. M. Islam, A. A. Ibrahim, A. H. Mutlag, "A Nature Inspired Heuristic Optimization Algorithm Based on Lightning," 2015 3rd International Conference on Artificial Intelligence, Modelling and Simulation (AIMS), pp. 9-14, 2015,
[CrossRef] [Web of Science Times Cited 2] [SCOPUS Times Cited 5]
 P. Agarwal and S. Mehta, " Nature-inspired algorithms: state-of-art, problems and prospects," International Journal of Computer Applications, vol. 100, no. 14, pp. 14-21, 2014.
 S. Akyol and B. Alatas, "The Current Swarm Intelligence Optimization Algorithms," Nevsehir Bilim ve Teknoloji Dergisi, vol. 1, no. 1, pp. 36-40, 2012.
 B. Akay and D. Karaboga, "A modified Artificial Bee Colony algorithm for real-parameter optimization," Information Sciences, vol. 192, no. Supplement C, pp. 120-142, 2012,
[CrossRef] [Web of Science Times Cited 674] [SCOPUS Times Cited 830]
 J. Chen, W. Yu, J. Tian, L. Chen, and Z. Zhou, "Image contrast enhancement using an artificial bee colony algorithm," Swarm and Evolutionary Computation, vol. 38, pp. 287-294, 2018,
[CrossRef] [Web of Science Times Cited 37] [SCOPUS Times Cited 56]
 S. G. Ahmad, C. S. Liew, E. U. Munir, T. F. Ang, and S. U. Khan, "A hybrid genetic algorithm for optimization of scheduling workflow applications in heterogeneous computing systems," Journal of Parallel and Distributed Computing, vol. 87, pp. 80-90, 2016,
[CrossRef] [Web of Science Times Cited 49] [SCOPUS Times Cited 62]
 S. Yilmaz and E. U. Kucuksille, "A new modification approach on bat algorithm for solving optimization problems," Applied Soft Computing, vol. 28, no. Supplement C, pp. 259-275, 2015,
[CrossRef] [Web of Science Times Cited 131] [SCOPUS Times Cited 154]
 M. S. Kiran, E. Ozceylan, M. Gunduz, and T. Paksoy, "A novel hybrid approach based on Particle Swarm Optimization and Ant Colony Algorithm to forecast energy demand of Turkey," Energy Conversion and Management, vol. 53, no. 1, pp. 75-83, 2012,
[CrossRef] [Web of Science Times Cited 128] [SCOPUS Times Cited 143]
 M. S. Kiran, E. Ozceylan, M. Gunduz, and T. Paksoy, "Swarm intelligence approaches to estimate electricity energy demand in Turkey," Knowledge-Based Systems, vol. 36, pp. 93-103, 2012,
[CrossRef] [Web of Science Times Cited 64] [SCOPUS Times Cited 69]
 M. Bayrak and O. Esen, "Forecasting Turkey's energy demand using artificial neural networks: Future Projection Based on an Energy Deficit," Journal of Applied Economic Sciences, vol. 2, no. 28, pp. 191-204, 2014.
 B. Cayir Ervural and B. Ervural, "Improvement of grey prediction models and their usage for energy demand forecasting," Journal of Intelligent & Fuzzy Systems, vol. 34, no. 4, pp. 2679-2688, 2018,
[CrossRef] [Web of Science Times Cited 8] [SCOPUS Times Cited 8]
 M. S. Kiran, "TSA: Tree-seed algorithm for continuous optimization," Expert Systems with Applications, vol. 42, no. 19, pp. 6686-6698, 2015,
[CrossRef] [Web of Science Times Cited 94] [SCOPUS Times Cited 109]
 M. Aslan, M. Beskirli, H. Kodaz, M.S. Kiran, "An improved tree seed algorithm for optimization problems," Int J Mach Learn Comput, vol. 8, no. 1, pp. 20-25, 2018,
[CrossRef] [SCOPUS Times Cited 11]
 M. Beskirli, "Performance Analysis of Tree Seed Algorithm in High Dimensional Test Functions," European Journal of Science and Technology, (Special Issue), pp. 93-101, 2019,
 M. F. Tefek and H. Uguz, " Solution of economic dispatch problem for wind-thermal power systems by a modified hybrid optimization method," Journal of the Faculty of Engineering and Architecture of Gazi University, vol. 34, no. 4, pp. 1871-1895, 2019,
[CrossRef] [Web of Science Times Cited 4] [SCOPUS Times Cited 2]
 A. Beskirli, D. Ozdemir, and H. Temurtas, "A comparison of a modified tree-seed algorithm for high-dimensional numerical functions," Neural Computing and Applications, pp. 1-35, 2019,
[CrossRef] [Web of Science Times Cited 5] [SCOPUS Times Cited 3]
 WECTNC, World Energy Council, Energy Report-2013, Ankara (in Turkish), ISSN: 1301-6318 (Ankara, Ocak). 2014.
 NS, "National Statistics, http://www.tuik.gov.tr (in Turkish)," 2016.
 M. F. Tefek and H. Uguz, "Estimation of Turkey Electric Energy Demand until the Year 2035 Using TLBO Algorithm" International Journal of Intelligent Systems and Applications in Engineering, vol. 4, pp. 48-52, 2016,
 M. Beskirli, H. Hakli, and H. Kodaz, "The energy demand estimation for Turkey using differential evolution algorithm," SÄdhanÄ, vol. 42, no. 10, pp. 1705-1715, 2017,
[CrossRef] [Web of Science Times Cited 7] [SCOPUS Times Cited 8]
 D. M. Toksari, "Ant colony optimization approach to estimate energy demand of Turkey," Energy Policy, vol. 35, no. 8, pp. 3984-3990, 2007,
[CrossRef] [Web of Science Times Cited 112] [SCOPUS Times Cited 122]
 H. Hakli and H. Uguz, "Estimating energy demand of turkey using bat algorithm model," in International Journal of Arts & Sciences, Prague, Czech Republic, 2014.
 A. Beskirli, M. Beskirli, H. Hakli, and H. Uguz, "Comparing energy demand estimation using artificial algae algorithm: The case of Turkey," Journal of Clean Energy Technologies, vol. 6, no. 4, 2018,
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