Günlük temelli orta vadeli şehir doğal gaz talebinin tek değişkenli istatistik teknikleri ile tahmini

Doğal gaz günümüzde enerji üretimi, ısınma ve pişirmede kullanılan en temel enerji kaynağıdır. Yaygın ağ yapısı ile birlikte evler, sanayi kuruluşları, santraller istedikleri anlarda bu enerjiye erişebilmektedir. Türkiye’de doğal gaz ithal bir enerji kaynağıdır ve uzun dönemli sözleşmeler ile anlaşmalar sağlanmaktadır. Uzun dönemli sözleşmeler karar vericiler tarafından yurtiçine arz edilir. Bu arz sürecinde doğal gaz tedarik şirketleri ve toptan satış şirketleri şehir dağıtım şirketleri ve sanayi kuruluşlarına yıllık sözleşmeler ile gaz arzı sağlarlar. Şirketler ve şehir dağıtım şirketleri bu sözleşmelerde aylık, yıl içinde de günlük tüketim talep tahminlerini bildirmekle yükümlüdür. Bu çalışma günlük ve aylık temelde orta vadeli doğal gaz talep tahminini tek değişkenli mevsimsellik içeren istatistiki yöntemler olan zaman serileri ayrıştırılması, Holt-Winters ve ARIMA/SARIMA modelleri ile gerçekleştirmiştir. Yapılan çalışmada günlük temelde 365 günlük, aylık temelde de 12 aylık tahmin bir anda gerçekleştirilmiştir. Doğal gaz tahmini sonucu günlük temelde en düşük hata yıl öncesi tahminde ARIMA(0,0,1)1(0,1,1)365 modeli ile 23,68% MAPE ile gerçekleşmiştir. Aylık dönüşümde ise en düşük tahmin modeli ARIMA(1,0,1)1(1,1,1)365 modeli ile 11,84% MAPE ile gerçekleşmiştir. Bu sonuçlar mevsimsel ARIMA modellerinin tek değişkenli teknikler arasında en uygun olduğunu göstermiştir. Bir anda çok sayıda tahmin yapılabilmesine imkan tanıması ve sonuçlarının kabul edilebilir olması bu tekniklerin karar vericiler tarafından kullanılabilmesine olanak tanımaktadır.

Anahtar Kelimeler:

Talep tahmini, doğal gaz, zaman serilerinin ayrıştırılması, Holt-Winters modeli, ARIMA/SARIMA modelleri

Daily basis mid-term demand forecast of city natural gas using univariate statistical techniques

Natural gas is the most basic energy source used today in energy production, heating and cooking. With its widespread network, houses, industrial enterprises and power plants can access this energy at any time. The natural gas used in Turkey is an imported energy source and its agreements is provided by long-term contracts. Long-term contracts are submitted to the domestic market by decision-makers. In this process, natural gas supply companies and wholesale companies, provide gas supplies to city distribution companies and industrial establishments with annual contracts (mid-term). City distribution or other companies are required to report monthly and year based daily consumption demand forecasts in these contracts. This paper studies forecasting of daily and monthly demand for mid-term natural gas as contract estimations using time series decomposition, Holt-Winters and ARIMA / SARIMA models, which are statistical methods, include univariate seasonality. In the study, 365-day forecast is performed on a daily basis and 12-month forecast is performed on a monthly basis at once. As a result of daily natural gas estimation, the lowest error is realized by ARIMA(0,0,1)1(0,1,1)365 model with 23.68% MAPE in the year ahead prediction. In the monthly conversion, the lowest estimation model is realized by ARIMA(1,0,1)1(1,1,1)365 model with 11.84% MAPE. The results show that seasonal ARIMA models are the most suitable among the univariate techniques. The fact that many predictions can be made at a time and the results are acceptable allow these techniques to be used by decision-makers.

Keywords:

Demand forecasting, natural gas, time series decomposition, Holt-Winters model, ARIMA/SARIMA models,

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