Enerji, bir ülkenin ekonomik, sosyal, teknolojik geliĢiminde önemli yere sahiptir. Bu sebepten, enerji modellenmesi üzerine birçok çalıĢmalar yapılmaktadır. Çünkü elde edilmiĢ güvenilir model gelecekteki enerji tüketimini tahmin etmeye olanak sağlamaktadır. Böylece, bu durum enerji kaynaklarının, maliyetin aĢırı harcanmasını veya enerji eksikliğini önleyebilir. Fakat, enerji tüketimine etki eden çok sayıda faktör ve onların belirsizlikleri nedeniyle, enerji talebi için kesin fiziksel model oluĢturmak mümkün değildir. Bu nedenle, enerji talebi, iklim faktörlerinin, çalıĢma koĢullarının vb. enerji tüketimine etki eden faktörlerin tüketime etkisini tanımlayan istatiksel modellere dayanarak hesaplanmaktadır. Modellemede son zamanlarda yapay zeka teknikleri de geniĢ bir Ģekilde kullanılmaktadır. Bu çalıĢmada, Türkiye‘nin elektrik enerjisi, TSp optimizasyon tekniği kullanılarak modellenmiĢtir.
Ülkenin sosyal ve ekonomik geliĢimi nedeniyle enerji talebi, özellikle de elektrik enerjisi talebi hızla büyümektedir. Türkiye, esas enerji kaynakları olarak kabul edilen doğalgaz ve petrol rezervlerine yeteri kadar sahip değildir. Ülkenin esas enerji kaynakları hidro ve linyitdir. Enerji yönetiminde Türkiye, çeĢitli sorunlarla karĢılaĢmaktadır. Bunlardan en önemlisi, doğal kaynakları kullanarak enerji üretimini arttırmak ve enerji ithalatını azaltmaktır. Güvenilir enerji modeline enerji yönetiminde ihtiyacın duyulması kaçınılmaz bir durumdur. Bu sebeple, 1984 yılında resmi olarak Türkiye‘de enerji modellenmesi çalıĢmaları yapılmaya baĢlanmıĢtır. Son zamanlarda ise AI tekniklerinin kullanıldığı çalıĢmalar literatürde geniĢ bir Ģekilde yer almaktadır.
Bu çalıĢmada, Türkiye‘nin elektrik enerjisi tüketiminin modellenmesi için AI tekniği olarak, metasezgisel popülasyon tabanlı TSA algoritmasının otomatik programlama için sunulan yeni versiyonu (TSp) kullanılmıĢtır. TSA, ağaç ve onların tohumları arasındaki iliĢkiye dayanan, popülasyon tabanlı, sezgisel bir algoritmadır. TSA‘nın geniĢletilmiĢ versiyonu olarak TSp, çözüm temsili için ağaç tabanlı çarpazlama kullanan otomatik programlama yaklaĢımıdır.
Elektrik enerjisi modelini elde etmek için elektrik enerjisi tüketimine etki eden indikatörler belirlenmiĢ ve onların 1992-2016 yılları arasındaki geçmiĢ verileri kullanılmıĢtır. Ġndikatörler olarak GDP, nüfus, ithalat, ihracat seçilmiĢtir. Sunulan yeni TSp metodu, enerji modellenmesi problemine uygulanmadan önce, 10 adet sembolik regresyon problemi üzerinde uygulanmıĢtır. Bazı fonksiyonlar üzerinde iyi sonuçlar elde eden TSp farklı problemler için uygulanabilir yaklaĢım olduğunu göstermiĢtir.
Ayrıca, TSp‘nin sembolik regresyon problemi üzerinde uygulanması enerji modellenmesi problemi için onun en uygun kontrol parametrelerinin seçilmesinide amaçlamaktadır. Bundan dolayı, TSp‘nin kontrol parametrelerinin farklı değerleri kullanılarak en iyi sonuç veren kontrol parametreleri enerji modellenmesi problemi için seçilmiĢtir (popülasyon ölçüsü 250, ST=0.1). TSp‘nin enerji modellenmesinde performansını araĢtırmak için GP aynı probleme uygulanmıĢtır. Her iki algoritmanın performansını daha iyi değerlendirmek için ise k katlamalı çapraz doğrulama yöntemi kullanılmıĢtır ve her katmanda GP ve TSp sonuçları kıyaslanmıĢtır. Veri seti 5 kata ayrılarak, her bir kat test verisi olarak kullanılırken, diğer 4 kat ise eğitim verisi olarak kullanılmıĢtır. Sonuçlara göre, TSp hem test, hem de eğitim verisi üzerinde daha iyi performans göstermiĢtir. Tahmin probleminde kullanılacak elektrik enerjisi modelini elde etmek için TSp, 1992-2016 yıllarındaki tüm verilere uygulanmıĢtır. %3,6 hataya sahip bu model, 2017-2025 yılları için 2 senaryo altında elektrik tüketimi tahmini değerleri üretmiĢtir. Senaryolar olarak, modellemede kullanılan 4 indikatörün son 6 ve son 24 yılda ortalama değiĢimi ele alınmıĢtır.
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ÖZGEÇMĠġ
KĠġĠSEL BĠLGĠLER
Adı Soyadı : PARVANA YUNUSOVA
Uyruğu : AZERBAYCANLI
Doğum Yeri ve Tarihi : AZERBAYCAN /18.10.1993
Telefon : +905346948993
Faks :
e-mail : pervane.yunsuova638@gmail.com EĞĠTĠM
Derece Adı, Ġlçe, Ġl Bitirme Yılı
Lise : 2011
Üniversite : Baku Devlet Universitesi Bilgisayar Bilimleri 2015
Yüksek Lisans : Selçuk Universitesi Bilgisayar Mühendisliği Devam ediyor Doktora :
Ġġ DENEYĠMLERĠ
Yıl Kurum Görevi
UZMANLIK ALANI YABANCI DĠLLER Ġngilizce, Türkce
YAYINLAR
Yunusova, P. ve Kiran, M. S., Tree-Seed Programming for Symbolic Regression, International Conference on Advanced Technologies, Computer Engineering and Science, Apr 26-28, 2019 Alanya, Turkey