TÜRKİYE'DE ENERJİ TALEBİNİ TAHMİN ETMEK İÇİN DOĞRUSAL FORM KULLANARAK GSA (YERÇEKİMİ ARAMA ALGORİTMASI) VE IWO (YABANİ OT OPTİMİZASYON ALGORİTMASI) TEKNİKLERİNİN UYGULANMASI
Öz
Bu çalışma, Türkiye'deki ekonomik göstergelere dayalı enerji talep tahmini ile ilgilidir. Enerji
talebini tahmin etmek için Yerçekimi Arama Algoritması (GSA) ve Yabani Ot Algoritması (IWO)
tekniklerine dayanan iki farklı model önerilmektedir. GSA yöntemi, Newton’un hareket ve yerçekimi
kanunlarından esinlenerek geliştirilmiş sezgisel optimizasyon algoritmasıdır. IWO algoritması ise
doğadaki yabani otların istilacı karakterlerinden esinlenen, evrimsel bir optimizasyon algoritmasıdır.
GSA ve IWO yöntemlerine dayalı enerji talep modelleri, gayri safi yurtiçi hâsıla (GSYİH), nüfus, ithalat
ve ihracat verilerini giriş parametresi şeklinde kullanan bir model olarak önerilmektedir. Önerilen
yöntemler doğrusal regresyon modeli kullanılarak geliştirilmiştir. Türkiye’nin gelecekteki enerji talebi
ise üç farklı senaryo altında tahmin edilmektedir. Önerilen tahmin modellerinden elde edilen deneysel
sonuçlar karşılaştırmalı olarak verilmiştir. 1979 ve 2005 yılları arasındaki veriler kullanılarak
gerçekleştirilen tahmin modelinde IWO literatürdeki diğer yöntemlerle de kıyaslanmış ve IWO yöntemi
en yüksek performansı verdiği görülmüştür. 1979 ve 2011 yılları arasındaki tüm veri seti kullanılarak
gerçekleştirilen tahmin modelinde ise GSA, IWO yöntemiyle karşılaştırılmış ve GSA daha iyi bir
performans elde etmiştir.
Anahtar Kelimeler
Enerji talebi Enerji talep tahmini GSA IWO Optimizasyon Türkiye
Kaynakça
- Beskirli M., Hakli H., Kodaz H., 2017, “The Energy Demand Estimation for Turkey using Differential Evolution Algorithm”, Sādhanā, pp. 1-11.
- Ceylan, H., Ceylan, H., Haldenbilen, S., Baskan, O., 2008, “Transport Energy Modeling with Meta-Heuristic Harmony Search Algorithm, an application to Turkey”, Energy Policy, Vol. 36(7), pp. 2527-2535.
- Dilaver, Z., Hunt, L. C., 2011, “Industrial Electricity Demand for Turkey: A Structural Time Series Analysis”, Energy Economics, Vol. 33(3), pp. 426-436.
- Ediger, V. Ş., Akar, S., 2007, “ARIMA Forecasting of Primary Energy Demand by fuel in Turkey”, Energy Policy, Vol. 35(3), pp. 1701-1708.
- Ediger, V. Ş., Tatlıdil, H., 2002, “Forecasting The Primary Energy Demand in Turkey and Analysis of Cyclic Patterns”, Energy Conversion and Management, Vol. 43(4), pp. 473-487.
- Erdogdu, E., 2007, “Electricity Demand Analysis using Cointegration and ARIMA Modelling: A case Study of Turkey”, Energy Policy, Vol. 35(2), pp. 1129-1146.
- Güneş, G., Aslan, E. “Yenilenebilir Enerji Kaynaklarının Kullanımı Ve Sürdürülebilir Turizme Olan Etkileri-Türkiye Örneği”
- Kankal, M., Akpınar, A., Kömürcü, M. İ., Özşahin, T. Ş., 2011, “Modeling and Forecasting of Turkey’s Energy Consumption Using Socio-Economic and Demographic Variables”, Applied Energy, Vol. 88(5), pp. 1927-1939.
- Kıran, M. S., Özceylan, E., Gündüz, M., Paksoy, T., 2012a, “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(1), pp. 75-83.
- Kıran, M. S., Özceylan, E., Gündüz, M., Paksoy, T., 2012b, “Swarm Intelligence Approaches to Estimate Electricity Energy Demand in Turkey”, Knowledge-Based Systems, Vol. 36, pp. 93-103.
- Mahmutoğlu, M., Öztürk, F., 2015, “Türkiye Elektrik Tüketimi Öngörüsü ve Bu Kapsamda Geliştirilebilecek Politika Önerileri”, Paper presented at the EY International Congress on Economics II (EYC2015), November 5-6, 2015, Ankara, Turkey.
- Mehrabian, A. R., Lucas, C., 2006, “A Novel Numerical Optimization Algorithm Inspired from Weed Colonization”, Ecological Informatics, Vol. 1(4), pp. 355-366.
- Oğurlu, H., 2011, Matematiksel Modelleme Kullanarak Türkiye'nin Uzun Dönem Elektrik Yük Tahmini, Yüksek Lisans Tezi, Selçuk Üniversitesi Fen Bilimleri Enstitüsü, Konya.
- Ozturk, H. K., Ceylan, H., Canyurt, O. E., Hepbasli, A., 2005, “Electricity Estimation Using Genetic Algorithm Approach: a Case Study of Turkey”, Energy, Vol. 30(7), pp. 1003-1012.
- Pourjafari, E., Mojallali, H. 2012 “Solving Nonlinear Equation Systems with a New Approach Based on Invasive Weed Optimization Algorithm and Clustering”, Swarm and Evolutionary Computation, Vol. 4, pp. 33-43.
- Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S., 2009, GSA: “A Gravitational Search Algorithm”, Information Sciences, Vol. 179(13), pp. 2232-2248.
- Sözen, A., Arcaklioğlu, E., Özkaymak, M., 2005, “Turkey’s Net Energy Consumption”, Applied Energy, Vol. 81(2), pp. 209-221.
- Tiris, M., 2005, “Global Trends for Energy”, Paper presented at the Turkish Workshop on Sustainable Development: Meeting the Challenges, JuØlich.
- Toksarı, M. D., 2007, “Ant Colony Optimization Approach to Estimate Energy Demand of Turkey”, Energy Policy, Vol. 35(8), pp. 3984-3990.
- Uguz, H., Hakli, H., Baykan, Ö. K., 2015, “A New Algorithm Based on Artificial Bee Colony Algorithm for Energy Demand Forecasting in Turkey”, Paper presented at the Advanced Computer Science Applications and Technologies (ACSAT), 2015 4th International Conference on.
- Ünler, A., 2008, “Improvement of Energy Demand Forecasts using Swarm Intelligence: The case of Turkey with Projections to 2025”, Energy Policy, Vol. 36(6), pp. 1937-1944.
- WECTNC., 2013, World Energy Council, Ankara (in Turkish), ISSN: 1301-63182014, Energy Report.
- WECTNC., 2014, World Energy Council, Ankara (in Turkish), ISSN: 1301-63182015, Energy Report.
- Yumurtaci, Z., Asmaz, E. 2004, “Electric Energy Demand of Turkey for the Year 2050”, Energy Sources, Vol. 26(12), pp. 1157-1164.
Implementation of GSA (Gravitation Search Algorithm) and IWO (Invasive Weed Optimization) for The Prediction of The Energy Demand in Turkey Using Linear Form
Öz
This paper deals with energy demand forecast based on economic indicators in Turkey.
Two different models based on the Gravity Search Algorithm (GSA) and Invasive Weed Optimization
Algorithm (IWO) techniques are proposed to estimate energy demand. GSA is heuristic optimization
algorithm inspired by Newton's laws of motion and gravity. The IWO algorithm is an evolutionary
optimization algorithm inspired by the invasive characters of weeds in the wild. Energy demand models
based on GSA and IWO methods are proposed using gross domestic product (GDP), population, import
and export data as input parameters. Proposed methods are developed using linear regression model.
Turkey's future energy demand is estimated under three different scenarios. The experimental results
obtained by prediction models are given comparatively. In the prediction model using data between
1979 and 2005, IWO is compared with other methods in the literature and IWO method shows the
highest performance. However, in the forecasting model obtained using the entire data set between 1979
and 2011, GSA is compared with the IWO method and GSA achieves better performance than IWO.
Anahtar Kelimeler
Energy demand Energy demand forecasting GSA IWO Optimization Turkey
Kaynakça
- Beskirli M., Hakli H., Kodaz H., 2017, “The Energy Demand Estimation for Turkey using Differential Evolution Algorithm”, Sādhanā, pp. 1-11.
- Ceylan, H., Ceylan, H., Haldenbilen, S., Baskan, O., 2008, “Transport Energy Modeling with Meta-Heuristic Harmony Search Algorithm, an application to Turkey”, Energy Policy, Vol. 36(7), pp. 2527-2535.
- Dilaver, Z., Hunt, L. C., 2011, “Industrial Electricity Demand for Turkey: A Structural Time Series Analysis”, Energy Economics, Vol. 33(3), pp. 426-436.
- Ediger, V. Ş., Akar, S., 2007, “ARIMA Forecasting of Primary Energy Demand by fuel in Turkey”, Energy Policy, Vol. 35(3), pp. 1701-1708.
- Ediger, V. Ş., Tatlıdil, H., 2002, “Forecasting The Primary Energy Demand in Turkey and Analysis of Cyclic Patterns”, Energy Conversion and Management, Vol. 43(4), pp. 473-487.
- Erdogdu, E., 2007, “Electricity Demand Analysis using Cointegration and ARIMA Modelling: A case Study of Turkey”, Energy Policy, Vol. 35(2), pp. 1129-1146.
- Güneş, G., Aslan, E. “Yenilenebilir Enerji Kaynaklarının Kullanımı Ve Sürdürülebilir Turizme Olan Etkileri-Türkiye Örneği”
- Kankal, M., Akpınar, A., Kömürcü, M. İ., Özşahin, T. Ş., 2011, “Modeling and Forecasting of Turkey’s Energy Consumption Using Socio-Economic and Demographic Variables”, Applied Energy, Vol. 88(5), pp. 1927-1939.
- Kıran, M. S., Özceylan, E., Gündüz, M., Paksoy, T., 2012a, “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(1), pp. 75-83.
- Kıran, M. S., Özceylan, E., Gündüz, M., Paksoy, T., 2012b, “Swarm Intelligence Approaches to Estimate Electricity Energy Demand in Turkey”, Knowledge-Based Systems, Vol. 36, pp. 93-103.
- Mahmutoğlu, M., Öztürk, F., 2015, “Türkiye Elektrik Tüketimi Öngörüsü ve Bu Kapsamda Geliştirilebilecek Politika Önerileri”, Paper presented at the EY International Congress on Economics II (EYC2015), November 5-6, 2015, Ankara, Turkey.
- Mehrabian, A. R., Lucas, C., 2006, “A Novel Numerical Optimization Algorithm Inspired from Weed Colonization”, Ecological Informatics, Vol. 1(4), pp. 355-366.
- Oğurlu, H., 2011, Matematiksel Modelleme Kullanarak Türkiye'nin Uzun Dönem Elektrik Yük Tahmini, Yüksek Lisans Tezi, Selçuk Üniversitesi Fen Bilimleri Enstitüsü, Konya.
- Ozturk, H. K., Ceylan, H., Canyurt, O. E., Hepbasli, A., 2005, “Electricity Estimation Using Genetic Algorithm Approach: a Case Study of Turkey”, Energy, Vol. 30(7), pp. 1003-1012.
- Pourjafari, E., Mojallali, H. 2012 “Solving Nonlinear Equation Systems with a New Approach Based on Invasive Weed Optimization Algorithm and Clustering”, Swarm and Evolutionary Computation, Vol. 4, pp. 33-43.
- Rashedi, E., Nezamabadi-Pour, H., Saryazdi, S., 2009, GSA: “A Gravitational Search Algorithm”, Information Sciences, Vol. 179(13), pp. 2232-2248.
- Sözen, A., Arcaklioğlu, E., Özkaymak, M., 2005, “Turkey’s Net Energy Consumption”, Applied Energy, Vol. 81(2), pp. 209-221.
- Tiris, M., 2005, “Global Trends for Energy”, Paper presented at the Turkish Workshop on Sustainable Development: Meeting the Challenges, JuØlich.
- Toksarı, M. D., 2007, “Ant Colony Optimization Approach to Estimate Energy Demand of Turkey”, Energy Policy, Vol. 35(8), pp. 3984-3990.
- Uguz, H., Hakli, H., Baykan, Ö. K., 2015, “A New Algorithm Based on Artificial Bee Colony Algorithm for Energy Demand Forecasting in Turkey”, Paper presented at the Advanced Computer Science Applications and Technologies (ACSAT), 2015 4th International Conference on.
- Ünler, A., 2008, “Improvement of Energy Demand Forecasts using Swarm Intelligence: The case of Turkey with Projections to 2025”, Energy Policy, Vol. 36(6), pp. 1937-1944.
- WECTNC., 2013, World Energy Council, Ankara (in Turkish), ISSN: 1301-63182014, Energy Report.
- WECTNC., 2014, World Energy Council, Ankara (in Turkish), ISSN: 1301-63182015, Energy Report.
- Yumurtaci, Z., Asmaz, E. 2004, “Electric Energy Demand of Turkey for the Year 2050”, Energy Sources, Vol. 26(12), pp. 1157-1164.
Ayrıntılar
| Birincil Dil | Türkçe |
|---|---|
| Konular | Mühendislik |
| Bölüm | Makaleler |
| Yazarlar | |
| Yayımlanma Tarihi | 1 Aralık 2018 |
| Yayımlandığı Sayı | Yıl 2018 Cilt: 6 Sayı: 4 |