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Forecasting Total Energy Demand with Grey Prediction Models: The Case of Turkey

Yıl 2020, , 771 - 782, 15.07.2020
https://doi.org/10.17714/gumusfenbil.676909

Öz

Energy demand forecasts are
needed for developing and emerging countries to be able to determine
sustainable energy policies. Grey prediction models can make successful
predictions with limited data without the need for any prior information. In
this study, Grey prediction models are considered for the total energy demand
of Turkey that shows significant economic and social development in recent
years. Grey prediction includes forecasting models based on time series and
cause-effect relationships. Four different grey prediction models including GM
(1,1) and Grey Verhulst from the time series models, and GM (0,N) and GM (1,N)
based on the cause-effect relationships were discussed. The purpose of using
two types of forecasting structures is to achieve reliable and strong forecasts
by capturing the recent trend with the time series and obtaining the change in
energy demand by cause-effect relationship. GM (1,1) and Grey Verhulst model
has been established with past total energy consumption data. By using
independent variables of GDP, population, import, export and building surface
area for GM (0, N) and GM (1, N) models, GM (0,6) and GM (1,6) models were
formed. All applied models have been compared according to performance
criteria, GM (1,1) and GM (1,6) have been designated to be superior models that
performed successful predictions. As a result; Turkey’s total energy demand has
been forecasted up to 2025 with GM (1,1) based on time series and GM (1,6)
including high and low scenarios.

Kaynakça

  • Aydin, G., 2014. Modeling of Energy Consumption Based on Economic And Demographic Factors: the Case of Turkey with Projections. Renewable and Sustainable Energy Reviews, 35, 382-389.
  • Bayramoğlu, T., Pabuçcu, H., ve Boz, F. Ç., 2017. Türkiye için Anfis Modeli ile Birincil Enerji Talep Tahmini. Ege Akademik Bakış, 17(3), 431-445.
  • Bessec, M., ve Fouquau, J., 2008. The Non-Linear Link between Electricity Consumption and Temperature in Europe: a Threshold Panel Approach. Energy Economics, 30(5), 2705-2721.
  • Boran, K., 2014. The Box Jenkins Approach to Forecast Net Electricity Consumption in Turkey. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 36(5), 515-524.
  • BP, 2019. Statistical Review of World Energy Workbook,: Https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html
  • Ceylan, H., ve Ozturk, H. K., 2004. Estimating Energy Demand of Turkey Based on Economic Indicators using Genetic Algorithm Approach. Energy Conversion and Management, 45(15-16), 2525-2537.
  • Es, H. A., 2019. Comparison of Direct and Iterative Grey Prediction Models for Natural Gas Demand, 3rd International Web Conference on Forecasting, November 2019, Giresun, Turkey, s.28-34.
  • Es, H. A., Hamzacebi, C., ve Firat, S. U. O., 2018. GRA-TRI: A Multicriteria Decision Aid Classification Method based on Grey Relational Analysis. The Journal of Grey System, 30(3), 1-13.
  • Es, H. A., Kalender, F. Y., ve Hamzaçebi, C., 2014. Yapay Sinir Ağlari ile Türkiye Net Enerji Talep Tahmini. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 29(3), 495-504.
  • Geem, Z. W., ve Roper, W. E., 2009. Energy Demand Estimation of South Korea Using Artificial Neural Network. Energy Policy, 37(10), 4049-4054.
  • Hamzacebi, C., ve Es, H. A., 2014. Forecasting the Annual Electricity Consumption of Turkey Using an Optimized Grey Model. Energy, 70, 165-171.
  • Hamzaçebi, C., Es, H.A. ve Çakmak, R., 2019. Forecasting of Turkey’s Monthly Electricity Demand by Seasonal Artificial Neural Network. Neural Computing and Applications 31, 2217–2231. He, Y., ve Lin, B., 2018. Forecasting China's Total Energy Demand and its Structure Using ADL-MIDAS Model. Energy, 151, 420-429.
  • Jiang, Y., Yao, Y., Deng, S., ve Ma, Z., 2004. Applying Grey Forecasting to Predicting the Operating Energy Performance ff Air Cooled Water Chillers. International Journal of Refrigeration, 27(4), 385-392.
  • Kavaklioglu, K., Ceylan, H., Ozturk, H. K., ve Canyurt, O. E., 2009. Modeling and Prediction of Turkey’s Electricity Consumption Using Artificial Neural Networks. Energy Conversion and Management, 50(11), 2719-2727.
  • Kucukali, S., ve Baris, K., 2010. Turkey’s Short-Term Gross Annual Electricity Demand Forecast by Fuzzy Logic Approach. Energy Policy, 38(5), 2438-2445.
  • Lee, Y. S., ve Tong, L. I., 2011. Forecasting Energy Consumption Using a Grey Model Improved by Incorporating Genetic Programming. Energy Conversion and Management, 52(1), 147-152.
  • Lin, C. T., ve Hsu, P. F., 2002. Forecast of Non-Alcoholic Beverage Sales in Taiwan Using the Grey Theory. Asia Pacific Journal of Marketing and Logistics, 14(4), 3-12.
  • Lin, Y., Liu S., 2004. A Historical Introduction to Grey Systems Theory. 2004 IEEE International Conference on Systems, Man and Cybernetics, 3, 2403-2408.
  • Liu, S., ve Forrest, J. Y. L., 2010. Grey Systems: Theory and Applications. Springer Science & Business Media, 379p.
  • Liu, S., ve Lin, Y., 2006. Grey Information: Theory and Practical Applications. Springer Science & Business Media, 508p.
  • Liu, X., Moreno, B., ve García, A. S., 2016. A Grey Neural Network and Input-Output Combined Forecasting Model. Primary Energy Consumption Forecasts in Spanish Economic Sectors. Energy, 115, 1042-1054.
  • Salcedo-Sanz, S., Muñoz-Bulnes, J., Portilla-Figueras, J. A., ve Del Ser, J., 2015. One-Year-Ahead Energy Demand Estimation from Macroeconomic Variables Using Computational Intelligence Algorithms. Energy Conversion and Management, 99, 62-71.
  • Salisu, A. A., ve Ayinde, T. O., 2016. Modeling Energy Demand: Some Emerging Issues. Renewable and Sustainable Energy Reviews, 54, 1470-1480.
  • Sánchez-Oro, J., Duarte, A., ve Salcedo-Sanz, S., 2016. Robust Total Energy Demand Estimation with a Hybrid Variable Neighborhood Search–Extreme Learning Machine Algorithm. Energy Conversion and Management, 123, 445-452.
  • Suganthi, L., ve Samuel, A. A., 2012. Energy Models for Demand Forecasting—a Review. Renewable and sustainable energy reviews, 16(2), 1223-1240.
  • Tseng, F. M., Yu, H. C., ve Tzeng, G. H., 2001. Applied Hybrid Grey Model to Forecast Seasonal Time Series. Technological Forecasting and Social Change, 67(2-3), 291-302.
  • TUİK, 2020, Türkiye İstatistik Kurumu: Temel İstatistik Göstergeler, Http://www.tuik.gov.tr/UstMenu.do?metod=temelist
  • Ünler, A., 2008. Improvement of Energy Demand Forecasts Using Swarm Intelligence: The Case of Turkey with Projections to 2025. Energy Policy, 36(6), 1937-1944.
  • Wang, Q., Li, S., ve Li, R., 2018. Forecasting Energy Demand in China And India: Using Single-Linear, Hybrid-Linear, and Non-Linear Time Series Forecast Techniques. Energy, 161, 821-831.
  • Wei, S., ve Yanfeng, X., 2017. Research on China's Energy Supply and Demand Using an Improved Grey-Markov Chain Model Based nn Wavelet Transform. Energy, 118, 969-984.
  • Wen, K.L., 2004. Grey Systems: Modeling and Prediction, Yang's Scientific Research Institute, YangSky Scientific Press, 253p.
  • Worldbank, 2020. Dünya Bankası İstatistikleri, Https://data.worldbank.org/indicator/SP.POP.TOTL?locations=TR
  • Xie, N. M., Yuan, C. Q., ve Yang, Y. J., 2015. Forecasting China’s Energy Demand and Self-Sufficiency Rate by Grey Forecasting Model and Markov Model. International Journal of Electrical Power and Energy Systems, 66, 1-8.

Gri Tahmin Modelleri ile Toplam Enerji Talep Tahmini: Türkiye Örneği

Yıl 2020, , 771 - 782, 15.07.2020
https://doi.org/10.17714/gumusfenbil.676909

Öz

Gelişen ve gelişmekte olan ülkelerin,
sürdürülebilir enerji politikaları belirleyebilmesinde enerji talep
tahminlerine ihtiyaç duyulmaktadır. Gri tahmin modelleri, önceden herhangi bir ön
bilgiye ihtiyaç duymadan sınırlı veri ile başarılı tahminler gerçekleştirebilmektedir.
Bu çalışmada, son dönemde önemli ekonomik ve sosyal gelişme gösteren
Türkiye’nin toplam enerji talebi için Gri tahmin modelleri dikkate alınmıştır.
Gri tahmin; zaman serisi ve sebep-sonuç ilişkisine dayalı çeşitli tahmin
modellerini içermektedir. Çalışma kapsamında; zaman serisi modellerinden
GM(1,1) ve Gri Verhulst ile sebep-sonuç ilişkisine dayalı GM(0,N) ve GM (1,N) modelleri
olmak üzere dört farklı gri tahmin modeli ele alınmıştır. İki çeşit tahmin
yapısının kullanılmasındaki amaç; son dönemdeki trendin zaman serisi ile
yakalanması ve enerji talebindeki değişimin sebep-sonuç ilişkisiyle elde
edilmesi sağlanarak güvenilir ve güçlü tahminlere ulaşmaktır. GM (1,1) ve Gri
Verhulst modelleri geçmiş toplam enerji tüketim verileri ile kurulmuştur. GM
(0,N) ve GM (1,N) modellerinde ise; GSYH, nüfus, ithalat, ihracat ve bina
yüzölçümü bağımsız değişkenleri kullanılarak GM (0,6) ve GM (1,6) modelleri
oluşturulmuştur. Kurulan tüm modeller performans ölçütlerine göre kıyaslanmış,
başarılı tahmin gerçekleştiren üstün modellerin GM (1,1) ve GM (1,6) olduğu belirlenmiştir.
Sonuç olarak; GM (1,1) ile zaman serisi ve GM (1,6) ile yüksek ve düşük senaryo
bazlı olmak üzere Türkiye toplam enerji talebi 2025 yılına kadar tahmin
edilmiştir.

Kaynakça

  • Aydin, G., 2014. Modeling of Energy Consumption Based on Economic And Demographic Factors: the Case of Turkey with Projections. Renewable and Sustainable Energy Reviews, 35, 382-389.
  • Bayramoğlu, T., Pabuçcu, H., ve Boz, F. Ç., 2017. Türkiye için Anfis Modeli ile Birincil Enerji Talep Tahmini. Ege Akademik Bakış, 17(3), 431-445.
  • Bessec, M., ve Fouquau, J., 2008. The Non-Linear Link between Electricity Consumption and Temperature in Europe: a Threshold Panel Approach. Energy Economics, 30(5), 2705-2721.
  • Boran, K., 2014. The Box Jenkins Approach to Forecast Net Electricity Consumption in Turkey. Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 36(5), 515-524.
  • BP, 2019. Statistical Review of World Energy Workbook,: Https://www.bp.com/en/global/corporate/energy-economics/statistical-review-of-world-energy.html
  • Ceylan, H., ve Ozturk, H. K., 2004. Estimating Energy Demand of Turkey Based on Economic Indicators using Genetic Algorithm Approach. Energy Conversion and Management, 45(15-16), 2525-2537.
  • Es, H. A., 2019. Comparison of Direct and Iterative Grey Prediction Models for Natural Gas Demand, 3rd International Web Conference on Forecasting, November 2019, Giresun, Turkey, s.28-34.
  • Es, H. A., Hamzacebi, C., ve Firat, S. U. O., 2018. GRA-TRI: A Multicriteria Decision Aid Classification Method based on Grey Relational Analysis. The Journal of Grey System, 30(3), 1-13.
  • Es, H. A., Kalender, F. Y., ve Hamzaçebi, C., 2014. Yapay Sinir Ağlari ile Türkiye Net Enerji Talep Tahmini. Gazi Üniversitesi Mühendislik-Mimarlık Fakültesi Dergisi, 29(3), 495-504.
  • Geem, Z. W., ve Roper, W. E., 2009. Energy Demand Estimation of South Korea Using Artificial Neural Network. Energy Policy, 37(10), 4049-4054.
  • Hamzacebi, C., ve Es, H. A., 2014. Forecasting the Annual Electricity Consumption of Turkey Using an Optimized Grey Model. Energy, 70, 165-171.
  • Hamzaçebi, C., Es, H.A. ve Çakmak, R., 2019. Forecasting of Turkey’s Monthly Electricity Demand by Seasonal Artificial Neural Network. Neural Computing and Applications 31, 2217–2231. He, Y., ve Lin, B., 2018. Forecasting China's Total Energy Demand and its Structure Using ADL-MIDAS Model. Energy, 151, 420-429.
  • Jiang, Y., Yao, Y., Deng, S., ve Ma, Z., 2004. Applying Grey Forecasting to Predicting the Operating Energy Performance ff Air Cooled Water Chillers. International Journal of Refrigeration, 27(4), 385-392.
  • Kavaklioglu, K., Ceylan, H., Ozturk, H. K., ve Canyurt, O. E., 2009. Modeling and Prediction of Turkey’s Electricity Consumption Using Artificial Neural Networks. Energy Conversion and Management, 50(11), 2719-2727.
  • Kucukali, S., ve Baris, K., 2010. Turkey’s Short-Term Gross Annual Electricity Demand Forecast by Fuzzy Logic Approach. Energy Policy, 38(5), 2438-2445.
  • Lee, Y. S., ve Tong, L. I., 2011. Forecasting Energy Consumption Using a Grey Model Improved by Incorporating Genetic Programming. Energy Conversion and Management, 52(1), 147-152.
  • Lin, C. T., ve Hsu, P. F., 2002. Forecast of Non-Alcoholic Beverage Sales in Taiwan Using the Grey Theory. Asia Pacific Journal of Marketing and Logistics, 14(4), 3-12.
  • Lin, Y., Liu S., 2004. A Historical Introduction to Grey Systems Theory. 2004 IEEE International Conference on Systems, Man and Cybernetics, 3, 2403-2408.
  • Liu, S., ve Forrest, J. Y. L., 2010. Grey Systems: Theory and Applications. Springer Science & Business Media, 379p.
  • Liu, S., ve Lin, Y., 2006. Grey Information: Theory and Practical Applications. Springer Science & Business Media, 508p.
  • Liu, X., Moreno, B., ve García, A. S., 2016. A Grey Neural Network and Input-Output Combined Forecasting Model. Primary Energy Consumption Forecasts in Spanish Economic Sectors. Energy, 115, 1042-1054.
  • Salcedo-Sanz, S., Muñoz-Bulnes, J., Portilla-Figueras, J. A., ve Del Ser, J., 2015. One-Year-Ahead Energy Demand Estimation from Macroeconomic Variables Using Computational Intelligence Algorithms. Energy Conversion and Management, 99, 62-71.
  • Salisu, A. A., ve Ayinde, T. O., 2016. Modeling Energy Demand: Some Emerging Issues. Renewable and Sustainable Energy Reviews, 54, 1470-1480.
  • Sánchez-Oro, J., Duarte, A., ve Salcedo-Sanz, S., 2016. Robust Total Energy Demand Estimation with a Hybrid Variable Neighborhood Search–Extreme Learning Machine Algorithm. Energy Conversion and Management, 123, 445-452.
  • Suganthi, L., ve Samuel, A. A., 2012. Energy Models for Demand Forecasting—a Review. Renewable and sustainable energy reviews, 16(2), 1223-1240.
  • Tseng, F. M., Yu, H. C., ve Tzeng, G. H., 2001. Applied Hybrid Grey Model to Forecast Seasonal Time Series. Technological Forecasting and Social Change, 67(2-3), 291-302.
  • TUİK, 2020, Türkiye İstatistik Kurumu: Temel İstatistik Göstergeler, Http://www.tuik.gov.tr/UstMenu.do?metod=temelist
  • Ünler, A., 2008. Improvement of Energy Demand Forecasts Using Swarm Intelligence: The Case of Turkey with Projections to 2025. Energy Policy, 36(6), 1937-1944.
  • Wang, Q., Li, S., ve Li, R., 2018. Forecasting Energy Demand in China And India: Using Single-Linear, Hybrid-Linear, and Non-Linear Time Series Forecast Techniques. Energy, 161, 821-831.
  • Wei, S., ve Yanfeng, X., 2017. Research on China's Energy Supply and Demand Using an Improved Grey-Markov Chain Model Based nn Wavelet Transform. Energy, 118, 969-984.
  • Wen, K.L., 2004. Grey Systems: Modeling and Prediction, Yang's Scientific Research Institute, YangSky Scientific Press, 253p.
  • Worldbank, 2020. Dünya Bankası İstatistikleri, Https://data.worldbank.org/indicator/SP.POP.TOTL?locations=TR
  • Xie, N. M., Yuan, C. Q., ve Yang, Y. J., 2015. Forecasting China’s Energy Demand and Self-Sufficiency Rate by Grey Forecasting Model and Markov Model. International Journal of Electrical Power and Energy Systems, 66, 1-8.
Toplam 33 adet kaynakça vardır.

Ayrıntılar

Birincil Dil Türkçe
Konular Mühendislik
Bölüm Makaleler
Yazarlar

Hüseyin Avni Es 0000-0003-4987-0173

Yayımlanma Tarihi 15 Temmuz 2020
Gönderilme Tarihi 18 Ocak 2020
Kabul Tarihi 14 Haziran 2020
Yayımlandığı Sayı Yıl 2020

Kaynak Göster

APA Es, H. A. (2020). Gri Tahmin Modelleri ile Toplam Enerji Talep Tahmini: Türkiye Örneği. Gümüşhane Üniversitesi Fen Bilimleri Dergisi, 10(3), 771-782. https://doi.org/10.17714/gumusfenbil.676909