The energy demand estimation for Turkey using differential evolution algorithm

Beşkirli, Mehmet; Haklı, Hüseyin; Kodaz, Halife

doi:10.1007/s12046-017-0724-7

Cited by 23 publications

(19 citation statements)

References 32 publications

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“…The DE has certain advantages over other methods such as its rapid operation, applicability to large-scale complex problems, and its requirement for a small number of control parameters. The DE has been applied in various fields including machine design [24], traffic flow models [25], pattern recognition [26], energy demand estimation [1], the training of artificial neural networks [27], the solution of chemical engineering problems [28] and the planning of unbalanced radial distribution systems [29].…”

Section: Differential Evolution Algorithmmentioning

confidence: 99%

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A new approach for wind turbine placement problem using modified differentialevolution algorithm

Haklı¹

2019

Turk J Elec Eng & Comp Sci

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Energy use is increasing worldwide with industrialization and advancing technology. Following this increase, renewable energy resources are increasingly preferred to reduce the costs of energy production. Wind energy is preferred as a renewable energy resource because it is clean and safe. Wind turbines are used to meet the demand for wind energy. They are placed close to each other to generate higher amounts of energy. However, the wake effect problem arises in these types of layouts, and this hinders the turbines from producing the desired yield. A modified differential evolution (MDE) algorithm was proposed in this study to solve the placement problem for wind turbines, and employed a binary-real-coded method -obtained by combining binary coding and real coding. The proposed method contains three different modifications: generation of the initial population, regeneration, and mutation. The effective distribution of the wind turbines on land was achieved with a preliminary operation proposed to generate the initial population.In addition, with the MDE method, population regeneration and elitism were carried out to increase the diversity of population and to preserve the success of the method. Finally, a mutation operation was performed on the individuals to alternate the presence or absence of wind turbines. To investigate the performance of the MDE method in solving the wind turbine placement problem, the method was applied to a study area of 2 x 2 km. The results were compared with those obtained with other methods used in the published literature for the wind turbine placement problem. The most successful and productive placement was achieved using the proposed method, and experimental results showed that the MDE is an efficient and successful tool to solve the wind turbine placement problem.

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Section: Differential Evolution Algorithmmentioning

confidence: 99%

“…Electrical energy demand increases with increasing population and developing technology. To meet the fundamental needs of humans and contribute to the economic growth of a country, electrical energy is required [1].…”

Section: Introductionmentioning

confidence: 99%

A new approach for wind turbine placement problem using modified differentialevolution algorithm

Haklı¹

2019

Turk J Elec Eng & Comp Sci

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“…Elmacı [12] genetik algoritma (GA), yapay arı kolonisi (ABC) ve parçacık sürü optimizasyonu (PSO) algoritmalarını kullanarak Türkiye'nin 2030 yılına kadar olan enerji ihtiyacını tahmin etmiştir. Ozturk, Ceylan, Canyurt ve Hepbasli [13], GA; Toksarı [14], karınca koloni optimizasyonu (ACO); Ünler [2], PSO; Ceylan, Ceylan, Haldenbilen ve Baskan [15], harmoni arama; Kıran, Özceylan, Gündüz ve Paksoy [16], PSO ve ACO; Uguz, Hakli ve Baykan [17], ABCVSS; Beskirli, Hakli ve Kodaz [18], diferansiyel evrim; Koç, Nureddin ve Kahramanlı [4] yabani ot (IWO) ve yerçekimi arama (GSA) algoritmalarını kullanarak çeşitli enerji tahmin çalışmaları yapmışlardır.…”

Section: Introductionunclassified

A new model based on vortex search algorithm for estimating energy demand of Turkey

Özkış¹

2020

Pamukkale J Eng Sci

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Bu çalışmada, Türkiye'nin enerji talebini tahmin etmek amacıyla Girdap Arama (Vortex Search, VS) algoritması temelli yeni bir doğrusal regresyon modeli geliştirilmiştir. Modelde Türkiye'deki gayri safi yurtiçi hâsıla (GSYİH), nüfus, ithalat ve ihracat verileri girdi parametrelerini; ortaya çıkan enerji talebi ise tahmin edilecek çıktıyı ifade etmektedir. 1979-2005 ve 1979-2011 yılları arasındaki veriler kullanılarak geliştirilen iki farklı tahmin modeli literatürdeki benzer çalışmalarla karşılaştırılmıştır. Elde edilen sonuçlar, geliştirilen VS modellerinin karşılaştırma yapılan modellerden daha başarılı veya rekabetçi sonuçlar elde ettiğini göstermiştir. Çalışmada son olarak, Türkiye'nin 2030 yılına kadar talep edeceği enerji miktarı, VS ve diğer modeller ile 3 farklı senaryo üzerinden tahmin edilmiştir. In this study, a new linear regression model based on Vortex Search (VS) algorithm was developed for estimating Turkey's energy demand. In this model, Turkey's gross domestic product (GDP), population, import and export data refer to input parameters; resulting energy demand refers to output to be estimated. Two different estimation models developed by using data between 1979-2005 and 1979-2011 were compared with similar studies in the literature. The results showed that the developed VS models obtained more successful or competitive results than the compared models. Finally in this study, the amount of energy that will be demanded in Turkey until 2030 was projected with VS and other models according to 3 different scenarios.

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“…Relationship between energy consumption and income was established by means of various mathematical formulas or various techniques for estimation of this procedure and thereby various models were formed to estimate primary energy demand of Turkey. These studies were conducted basing on statistical techniques [5][6][7][8][9][10], artificial intelligence techniques [11][12][13] and intuitional techniques [1,4,[14][15][16][17][18][19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Modified Gravitational Search Algorithm for Energy Demand Estimation of Turkey

Beşkirli

Tefek

Uğuz

2019

Bitlis Eren Üniversitesi Fen Bilimleri Dergisi

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Estimation of energy demand beforehand is a quite significant problem in respect of economy and sources of country. In this study, Gravitational Search Algorithm (GSA) was modified by making some innovations in GSA and called as Modified Gravitational Search Algorithm (MGSA). Energy demand estimation is conducted through the relationship between the increase in economic indicators in Turkey and energy consumption. Estimation was actualized by using gross domestic product (GSYH), importation, exportation and demography for energy demand estimation and both linear and exponential equations. Energy demand between the years 2017-2037 was predicted by using the data belong to 1997-2011. The years between 2012 and 2016 were used as test data. It was observed that the results acquired via MGSA estimate better compared to GSA results.

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The energy demand estimation for Turkey using differential evolution algorithm

Cited by 23 publications

References 32 publications

A new approach for wind turbine placement problem using modified differentialevolution algorithm

A new approach for wind turbine placement problem using modified differentialevolution algorithm

A new model based on vortex search algorithm for estimating energy demand of Turkey

Modified Gravitational Search Algorithm for Energy Demand Estimation of Turkey

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