Forecasting of daily natural gas consumption on regional basis in Turkey using various computational methods


Taşpinar F., Çelebi N., Tutkun N.

Energy and Buildings, cilt.56, ss.23-31, 2013 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 56
  • Basım Tarihi: 2013
  • Doi Numarası: 10.1016/j.enbuild.2012.10.023
  • Dergi Adı: Energy and Buildings
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.23-31
  • Anahtar Kelimeler: Forecasting methodology, Natural gas consumption, Neural networks, SARIMAX, Time series models
  • İstanbul Ticaret Üniversitesi Adresli: Hayır

Özet

It is widely accepted that natural gas is a clean energy source that can be used to meet energy demand for heating and industrial purpose among the fossil fuels and its usage remarkably increases in order to maintain a clean environment in many countries in the world. It is fact that this makes energy investment planning in a country or region highly important for suitable economic development as well as environmental aspect. Therefore, energy demand for various sectors should be estimated in the frame of short-term energy policy. For accurate estimation of short-term energy demand a limited number of computational methods are employed by using the 4 yearly measured natural gas consumption values. Among these methods, the ANN and time series are widely used for short-term estimation of natural gas consumption in Turkey's certain regions. In this study, multilayer perceptron the ANNs with time series approach is proposed to forecast short-term natural gas consumption. Meteorological data (moisture, atmospheric pressure, wind speed and ambient temperature) obtained from the regional gas distribution company and the local meteorology office in last 4 years to construct well-tuned algorithm. Although the number of data was small, the proposed algorithm works well to forecast the short-term natural gas consumption and produces encouraging and meaningful outcomes for future energy investment policy. © 2012 Elsevier B.V. All rights reserved.