Bu çalışmada, yapay sinir ağları (YSA) ile Türkiye net enerji talebi tahmin edilmiştir. Türkiye net enerji talebini tahmin etmek için 1970-2010 yılları arasındaki Gayri Safi Yurtiçi Hâsıla (GSYH) , nüfus, ithalat, ihracat, bina yüz ölçümü ve taşıt sayısı değişken verileri YSA modelinin girdisi olarak kullanılmıştır. Kurulan YSA modelinin tahmin performansı, çoklu doğrusal regresyon tekniği ile karşılaştırmalı olarak ortaya konmuştur. Yapılan karşılaştırmalar, YSA’nın üstünlüğünü göstermektedir. Kabul edilebilir ve yüksek doğruluktaki YSA modeli ile 2011-2025 yılları arası Türkiye net enerji talebi tahmin edilmiştir.

Natural gas stands out among fossil fuels because it is relatively cleaner. It is also an important energy source type for several fields such as electricity production, industry, and heating, etc. Due to the poor capacity of Turkey in terms of natural gas sources, the demand is supplied by producer countries. Hence, accurate forecasting for the demand is of critical importance for Turkey, which imports 99% of its natural gas consumption. In the current literature about demand forecasting, most studies were conducted on an annual basis. However, the seasonal effect on the demand for natural gas cannot be foreseen through annual studies. Besides, to deal with some situations such as seasonal balancing, peak shaving, and gas supply shortage in monthly demand, forecasting models that capture the seasonal trend are needed. Therefore, in this study, a new grey seasonal forecast model has been presented and Turkey’s monthly natural gas demand was predicted via the proposed model. Performance of that model was compared with SGM(1,1) and SARIMA (p,d,q) x (P,D,Q)^s. The obtained results show the superiority of the proposed model. By using this model, Turkey’s monthly natural gas demand was forecasted up until the year 2025. The proposed model allows us to capture seasonal patterns more successfully. In case this seasonal behavior continues, Turkey’s natural gas demand is expected to increase by %20 until 2025. At this point, the outcomes of the study provide important information to decision-makers to be able to determine reliable and stable energy policies.