Araştırma Makalesi
BibTex RIS Kaynak Göster

IMPROVING TURKEY'S NATURAL GAS DEMAND FORECASTS: A DATA ANALYTICS APPROACH

Yıl 2023, Cilt: 21 Sayı: 3, 312 - 332, 14.10.2023

Ezgi Avcı

https://doi.org/10.11611/yead.1323635

Öz

As the world population and consequently energy demand is rapidly increasing, primary energy sources are needed for sustainable development. One fifth of the world's energy needs are provided by natural gas, due to it being the cleanest burning fossil fuel. However, as a fossil fuel, natural gas is a limited resource and requires efficient use. Therefore, planning for natural gas consumption is of great importance. The most critical input for this planning is natural gas demand forecasts. Due to the increasing importance of the subject, numerous studies on natural gas demand forecasting have been conducted in the literature. However, the scientific studies carried out in Turkey are quite limited. In this article, the current demand forecast situation in Turkey is revealed, and a data analytics approach based on big data is developed and proposed.

Anahtar Kelimeler

Natural Gas Demand Forecast Big Data Analytics Decision Support Systems Policy Design

Destekleyen Kurum

-

Proje Numarası

-

Teşekkür

-

Kaynakça

  • Aras, H., Aras, N. 2004 “Forecasting Residential Natural Gas Demand”, Energy Sources, 26(5), 463-472.
  • Aydin, G. 2015 “The Modeling and Projection of Primary Energy Consumption by The Sources”, Energy Sources A, 10(1), 67-74.
  • Azadeh, A., Zarrin, M., Beik, H.R. and Bioki, T.A. 2015 “A Neuro-Fuzzy Algorithm for Improved Gas Consumption Forecasting with Economic, Environmental And IT/IS Indicators”, Journal of Petroleum Science and Engineering, 133, 716-739.
  • Balestra, P. and Nerlove, M. 1966 “Pooling Cross Section and Time Series Data in The Estimation of a Dynamic Model: The Demand for Natural Gas”, Econometrica (pre-1986), 34(3): 585.
  • Baltagi, B. H., Bresson, G.and Pirotte, A. 2002 “Comparison of Forecast Performance for Homogeneous, Heterogeneous and Shrinkage Estimators: Some Empirical Evidence from US Electricity and Natural-Gas Consumption”, Economics Letters, 76(3): 375-382.
  • Beccali M, Cellura, M., Brano, V. L. and Marvuglia, A. 2004 “Forecasting Daily Urban Electric Load Profiles Using Artificial Neural Networks”, Energy Conversion And Management, 45(18-19): 2879-2900.
  • Berndt, E. R. and Watkins, G. C. 1977 “Demand for Natural Gas: Residential and Commercial Markets in Ontario and British Columbia”, Canadian Journal of Economics, 97-111.
  • Demirel, Ö. F., Zaim, S., Çalişkan, A. and Özuyar, P. 2012 “Forecasting Natural Gas Consumption in Istanbul Using Neural Networks and Multivariate Time Series Methods”, Turkish Journal of Electrical Engineering & Computer Sciences, 20(5): 695-711.
  • Dombaycı, Ö. A. 2010 “The Prediction of Heating Energy Consumption in A Model House by Using Artificial Neural Networks in Denizli–Turkey”, Advances in Engineering Software, 41(2): 141-147.
  • Durmayaz, A., Kadıoǧlu, M. and Şen, Z. 2000 “An Application of The Degree-Hours Method to Estimate the Residential Heating Energy Requirement and Fuel Consumption in Istanbul”, Energy, 25(12): 1245-1256.
  • Fagiani, M., Squartini, S., Gabrielli, L., Spinsante, S. and Piazza, F. 2015 “A Review of Datasets and Load Forecasting Techniques for Smart Natural Gas Water Grids: Analysis and Experiments”, Neurocomputing, 170: 448-465. Fan, Y. and Xia, Y. 2012 “Exploring Energy Consumption and Demand in China”, Energy, 40: 23-30. Görücü, F. B. 2004. “Evaluation And Forecasting of Gas Consumption by Statistical Analysis”, Energy Sources, 26(3): 267-276.
  • Gracias, A.C., Lourenco, S.R. and Rafikov, M. 2012 “Estimation of Natural Gas Production, Import and Consumption in Brazil Based on Three Mathematical Models”, Natural Resources, 3: 42-47.
  • Gutiérrez, R., Nafidi, A. and Sánchez, R. G. 2005 “Forecasting Total Natural-Gas Consumption In Spain By Using The Stochastic Gompertz Innovation Diffusion Model”, Applied Energy, 80(2): 115-124.
  • Gümrah, F., Katircioglu, D., Aykan, Y., Okumus, S. and Kilincer, N. 2001 “Modeling of Gas Demand Using Degree-Day Concept: A Case Study for Ankara”, Energy sources, 23(2): 101-114.
  • Ivezić, D. 2006 “Short-Term Natural Gas Consumption Forecast”, FME Transactions, 34(3): 165-169.
  • Kaynar, O., Yilmaz, I. and Demirkoparan, F. 2010 “Forecasting of Natural Gas Consumption with Neural Network and Neuro Fuzzy System”, In EGU General Assembly Conference Abstracts (Vol. 12, p. 7781).
  • Khan, MA. 2015 “Modeling and Forecasting the Demand for Natural Gas”, Renewable and Sustainable Energy Reviews, 49: 1145-1159.
  • Kizilaslan, R. and Karlik, B. 2009 “Combination of Neural Networks Forecasters for Monthly Natural Gas Consumption Prediction”, Neural network world, 19(2): 191.
  • Lim, H. L. and Brown, R. H. 2001. “Gas Load Forecasting Model Input Factor Identification Using a Genetic Algorithm”. In Proceedings of the 44th IEEE 2001 Midwest Symposium on Circuits and Systems. MWSCAS 2001 (Cat. No. 01CH37257) (Vol. 2, pp. 670-673). IEEE.
  • Maddala, G. S., Trost, R. P., Li, H. and Joutz, F. 1997 “Estimation of Short-Run and Long-Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators”, Journal of Business & Economic Statistics, 15(1): 90-100.
  • Merkel, G., Povinelli, R. and Brown, R. 2018 “Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression”, Energies, 11(8): 2008.
  • Panella, M., Barcellona, F. and D’Ecclessia, R.L. 2012 “Forecasting Energy Commodity Prices Using Neural Networks”, Research Article. DOI:10.1115/2012/289810.
  • Pang, B. 2012 “The Impact of Additional Weather Inputs on Gas Load Forecasting”.
  • Pappas, S. S., Ekonomou, L., Karamousantas, D. C., Chatzarakis, G. E., Katsikas, S. K. and Liatsis, P. 2008 “Electricity Demand Loads Modelling Using Autoregressive Moving Average (ARMA) Models”, Energy, 33(9): 1353-1360.
  • Payne, J. E., Loomis, D. G. and Wilson, R. 2011 “Residential Natural Gas Demand in Illinois: Evidence from The ARDL Bounds Testing Approach”. Journal of Regional Analysis and Policy, 41(1100-2016-89863), 138.
  • Potočnik, P., Thaler, M., Govekar, E., Grabec, I. and Poredoš, A. 2007 “Forecasting Risks Of Natural Gas Consumption In Slovenia”. Energy Policy, 35(8): 4271-4282.
  • Sarak, H. and Satman, A. 2003 “The Degree-Day Method to Estimate the Residential Heating Natural Gas Consumption in Turkey: A Case Study”, Energy, 28(9): 929-939.
  • Soldo, B. 2012 “Forecasting Natural Gas Consumption”, Applied Energy, 92: 26-37.
  • Suganthi, L., and Samuel, A.A. 2012 “Energy Models for Demand Forecasting-A Review”, Renewable and Sustainable Energy Reviews, 16:1223-1240.
  • Szoplik, J. 2015 “Forecasting of Natural Gas Consumption with Artificial Neural Networks”, Energy, 85: 208-220.
  • Tamba, J. G., Essiane, S. N., Sapnken, E. F., Koffi, F. D., Nsouandélé, J. L., Soldo and B., Njomo, D. 2018 “Forecasting Natural Gas: A Literature Survey”, International Journal of Energy Economics and Policy, 8(3): 216-249.
  • Taşpınar, F., Celebi, N. and Tutkun, N. 2013 “Forecasting of Daily Natural Gas Consumption on Regional Basis in Turkey Using Various Computational Methods”, Energy and Buildings, 56: 23-31.
  • Vitullo, S., Brown, R. H., Corliss, G. F. andMarx, B. M. 2009 “Mathematical Models For Natural Gas Forecasting”, Canadian Applied Mathematics Quarterly.
  • Voudouris, V., Matsumoto, K., Sedgwick, J., Rigby, R., Stasinopoulos, D. and Jefferson, M. 2014 “Exploring the Production of Natural Gas Through the Lenses of The ACEGES Model”, Energy Policy, 64: 124-133.
  • Xiong, P.P., Dang, Y.G., Yao, T.X. and Wang, Z.X. 2014 “Optimal Modeling and Forecasting of The Energy Consumption and Production In China”, Energy, 77: 623-634.

Yıl 2023, Cilt: 21 Sayı: 3, 312 - 332, 14.10.2023

Ezgi Avcı

https://doi.org/10.11611/yead.1323635

Öz

Proje Numarası

-

Kaynakça

  • Aras, H., Aras, N. 2004 “Forecasting Residential Natural Gas Demand”, Energy Sources, 26(5), 463-472.
  • Aydin, G. 2015 “The Modeling and Projection of Primary Energy Consumption by The Sources”, Energy Sources A, 10(1), 67-74.
  • Azadeh, A., Zarrin, M., Beik, H.R. and Bioki, T.A. 2015 “A Neuro-Fuzzy Algorithm for Improved Gas Consumption Forecasting with Economic, Environmental And IT/IS Indicators”, Journal of Petroleum Science and Engineering, 133, 716-739.
  • Balestra, P. and Nerlove, M. 1966 “Pooling Cross Section and Time Series Data in The Estimation of a Dynamic Model: The Demand for Natural Gas”, Econometrica (pre-1986), 34(3): 585.
  • Baltagi, B. H., Bresson, G.and Pirotte, A. 2002 “Comparison of Forecast Performance for Homogeneous, Heterogeneous and Shrinkage Estimators: Some Empirical Evidence from US Electricity and Natural-Gas Consumption”, Economics Letters, 76(3): 375-382.
  • Beccali M, Cellura, M., Brano, V. L. and Marvuglia, A. 2004 “Forecasting Daily Urban Electric Load Profiles Using Artificial Neural Networks”, Energy Conversion And Management, 45(18-19): 2879-2900.
  • Berndt, E. R. and Watkins, G. C. 1977 “Demand for Natural Gas: Residential and Commercial Markets in Ontario and British Columbia”, Canadian Journal of Economics, 97-111.
  • Demirel, Ö. F., Zaim, S., Çalişkan, A. and Özuyar, P. 2012 “Forecasting Natural Gas Consumption in Istanbul Using Neural Networks and Multivariate Time Series Methods”, Turkish Journal of Electrical Engineering & Computer Sciences, 20(5): 695-711.
  • Dombaycı, Ö. A. 2010 “The Prediction of Heating Energy Consumption in A Model House by Using Artificial Neural Networks in Denizli–Turkey”, Advances in Engineering Software, 41(2): 141-147.
  • Durmayaz, A., Kadıoǧlu, M. and Şen, Z. 2000 “An Application of The Degree-Hours Method to Estimate the Residential Heating Energy Requirement and Fuel Consumption in Istanbul”, Energy, 25(12): 1245-1256.
  • Fagiani, M., Squartini, S., Gabrielli, L., Spinsante, S. and Piazza, F. 2015 “A Review of Datasets and Load Forecasting Techniques for Smart Natural Gas Water Grids: Analysis and Experiments”, Neurocomputing, 170: 448-465. Fan, Y. and Xia, Y. 2012 “Exploring Energy Consumption and Demand in China”, Energy, 40: 23-30. Görücü, F. B. 2004. “Evaluation And Forecasting of Gas Consumption by Statistical Analysis”, Energy Sources, 26(3): 267-276.
  • Gracias, A.C., Lourenco, S.R. and Rafikov, M. 2012 “Estimation of Natural Gas Production, Import and Consumption in Brazil Based on Three Mathematical Models”, Natural Resources, 3: 42-47.
  • Gutiérrez, R., Nafidi, A. and Sánchez, R. G. 2005 “Forecasting Total Natural-Gas Consumption In Spain By Using The Stochastic Gompertz Innovation Diffusion Model”, Applied Energy, 80(2): 115-124.
  • Gümrah, F., Katircioglu, D., Aykan, Y., Okumus, S. and Kilincer, N. 2001 “Modeling of Gas Demand Using Degree-Day Concept: A Case Study for Ankara”, Energy sources, 23(2): 101-114.
  • Ivezić, D. 2006 “Short-Term Natural Gas Consumption Forecast”, FME Transactions, 34(3): 165-169.
  • Kaynar, O., Yilmaz, I. and Demirkoparan, F. 2010 “Forecasting of Natural Gas Consumption with Neural Network and Neuro Fuzzy System”, In EGU General Assembly Conference Abstracts (Vol. 12, p. 7781).
  • Khan, MA. 2015 “Modeling and Forecasting the Demand for Natural Gas”, Renewable and Sustainable Energy Reviews, 49: 1145-1159.
  • Kizilaslan, R. and Karlik, B. 2009 “Combination of Neural Networks Forecasters for Monthly Natural Gas Consumption Prediction”, Neural network world, 19(2): 191.
  • Lim, H. L. and Brown, R. H. 2001. “Gas Load Forecasting Model Input Factor Identification Using a Genetic Algorithm”. In Proceedings of the 44th IEEE 2001 Midwest Symposium on Circuits and Systems. MWSCAS 2001 (Cat. No. 01CH37257) (Vol. 2, pp. 670-673). IEEE.
  • Maddala, G. S., Trost, R. P., Li, H. and Joutz, F. 1997 “Estimation of Short-Run and Long-Run Elasticities of Energy Demand from Panel Data Using Shrinkage Estimators”, Journal of Business & Economic Statistics, 15(1): 90-100.
  • Merkel, G., Povinelli, R. and Brown, R. 2018 “Short-Term Load Forecasting of Natural Gas with Deep Neural Network Regression”, Energies, 11(8): 2008.
  • Panella, M., Barcellona, F. and D’Ecclessia, R.L. 2012 “Forecasting Energy Commodity Prices Using Neural Networks”, Research Article. DOI:10.1115/2012/289810.
  • Pang, B. 2012 “The Impact of Additional Weather Inputs on Gas Load Forecasting”.
  • Pappas, S. S., Ekonomou, L., Karamousantas, D. C., Chatzarakis, G. E., Katsikas, S. K. and Liatsis, P. 2008 “Electricity Demand Loads Modelling Using Autoregressive Moving Average (ARMA) Models”, Energy, 33(9): 1353-1360.
  • Payne, J. E., Loomis, D. G. and Wilson, R. 2011 “Residential Natural Gas Demand in Illinois: Evidence from The ARDL Bounds Testing Approach”. Journal of Regional Analysis and Policy, 41(1100-2016-89863), 138.
  • Potočnik, P., Thaler, M., Govekar, E., Grabec, I. and Poredoš, A. 2007 “Forecasting Risks Of Natural Gas Consumption In Slovenia”. Energy Policy, 35(8): 4271-4282.
  • Sarak, H. and Satman, A. 2003 “The Degree-Day Method to Estimate the Residential Heating Natural Gas Consumption in Turkey: A Case Study”, Energy, 28(9): 929-939.
  • Soldo, B. 2012 “Forecasting Natural Gas Consumption”, Applied Energy, 92: 26-37.
  • Suganthi, L., and Samuel, A.A. 2012 “Energy Models for Demand Forecasting-A Review”, Renewable and Sustainable Energy Reviews, 16:1223-1240.
  • Szoplik, J. 2015 “Forecasting of Natural Gas Consumption with Artificial Neural Networks”, Energy, 85: 208-220.
  • Tamba, J. G., Essiane, S. N., Sapnken, E. F., Koffi, F. D., Nsouandélé, J. L., Soldo and B., Njomo, D. 2018 “Forecasting Natural Gas: A Literature Survey”, International Journal of Energy Economics and Policy, 8(3): 216-249.
  • Taşpınar, F., Celebi, N. and Tutkun, N. 2013 “Forecasting of Daily Natural Gas Consumption on Regional Basis in Turkey Using Various Computational Methods”, Energy and Buildings, 56: 23-31.
  • Vitullo, S., Brown, R. H., Corliss, G. F. andMarx, B. M. 2009 “Mathematical Models For Natural Gas Forecasting”, Canadian Applied Mathematics Quarterly.
  • Voudouris, V., Matsumoto, K., Sedgwick, J., Rigby, R., Stasinopoulos, D. and Jefferson, M. 2014 “Exploring the Production of Natural Gas Through the Lenses of The ACEGES Model”, Energy Policy, 64: 124-133.
  • Xiong, P.P., Dang, Y.G., Yao, T.X. and Wang, Z.X. 2014 “Optimal Modeling and Forecasting of The Energy Consumption and Production In China”, Energy, 77: 623-634.
Toplam 35 adet kaynakça vardır.

Ayrıntılar

Birincil Dil İngilizce
Konular Politika ve Yönetim (Diğer)
Bölüm Makaleler
Yazarlar

Ezgi Avcı 0000-0002-9826-1027

Proje Numarası -
Erken Görünüm Tarihi 17 Ekim 2023
Yayımlanma Tarihi 14 Ekim 2023
Yayımlandığı Sayı Yıl 2023 Cilt: 21 Sayı: 3

Kaynak Göster

APA Avcı, E. (2023). IMPROVING TURKEY’S NATURAL GAS DEMAND FORECASTS: A DATA ANALYTICS APPROACH. Journal of Management and Economics Research, 21(3), 312-332. https://doi.org/10.11611/yead.1323635
AMA Avcı E. IMPROVING TURKEY’S NATURAL GAS DEMAND FORECASTS: A DATA ANALYTICS APPROACH. Journal of Management and Economics Research. Ekim 2023;21(3):312-332. doi:10.11611/yead.1323635
Chicago Avcı, Ezgi. “IMPROVING TURKEY’S NATURAL GAS DEMAND FORECASTS: A DATA ANALYTICS APPROACH”. Journal of Management and Economics Research 21, sy. 3 (Ekim 2023): 312-32. https://doi.org/10.11611/yead.1323635.
EndNote Avcı E (01 Ekim 2023) IMPROVING TURKEY’S NATURAL GAS DEMAND FORECASTS: A DATA ANALYTICS APPROACH. Journal of Management and Economics Research 21 3 312–332.
IEEE E. Avcı, “IMPROVING TURKEY’S NATURAL GAS DEMAND FORECASTS: A DATA ANALYTICS APPROACH”, Journal of Management and Economics Research, c. 21, sy. 3, ss. 312–332, 2023, doi: 10.11611/yead.1323635.
ISNAD Avcı, Ezgi. “IMPROVING TURKEY’S NATURAL GAS DEMAND FORECASTS: A DATA ANALYTICS APPROACH”. Journal of Management and Economics Research 21/3 (Ekim 2023), 312-332. https://doi.org/10.11611/yead.1323635.
JAMA Avcı E. IMPROVING TURKEY’S NATURAL GAS DEMAND FORECASTS: A DATA ANALYTICS APPROACH. Journal of Management and Economics Research. 2023;21:312–332.
MLA Avcı, Ezgi. “IMPROVING TURKEY’S NATURAL GAS DEMAND FORECASTS: A DATA ANALYTICS APPROACH”. Journal of Management and Economics Research, c. 21, sy. 3, 2023, ss. 312-3, doi:10.11611/yead.1323635.
Vancouver Avcı E. IMPROVING TURKEY’S NATURAL GAS DEMAND FORECASTS: A DATA ANALYTICS APPROACH. Journal of Management and Economics Research. 2023;21(3):312-3.