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Journals / International Advanced Researches and Engineering Journal / 2021 / Cilt: 5 - Sayı: 1

GMDH-type neural network-based monthly electricity demand forecasting of Turkey

Ali Volkan AKKAYA
Pages
53–60
Publication Date
2022-10-19
DOI
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AI Summary & Key Takeaways

AI Insight
⚡ TL;DR(Too Long; Didn't Read)

The test results showed that the developed model was very close to actual values.

📌 3 Key Takeaways

🎯
Objective & Hypothesis:In this study, it was aimed to develop an accurate forecasting model for the monthly electricity demand of Turkey in the medium-term.
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Key Findings & Data:A large dataset containing monthly electricity demand was considered for the period of 2003-2018.
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Conclusion & Impact:The test results showed that the developed model was very close to actual values.
🔬Design:Modeling & Simulation
📍Scope:2003-2018 Verileri
⚙️Variables:Electricity demand · Forecasting · GMDH neural network

Abstract

In this study, it was aimed to develop an accurate forecasting model for the monthly electricity demand of Turkey in the medium-term. For this purpose, the Group Method of Data Handling (GMDH)-type Neural Network (NN) approach was used to structure a nonlinear time-series based forecasting model. A large dataset containing monthly electricity demand was considered for the period of 2003-2018. The developed model was tested in the period of 2019/01-2019/11 in order to determine the generalization ability of the model. The test results showed that the developed model was very close to actual values. The obtained test performances were 2.10 % for mean absolute percentage error (MAPE), 2.36 % for root mean square percentage error (RMSPE) and 0.869 for coefficient of determination (R2). In addition, results of the proposed GMDH-type NN model were compared with the forecasting results of a literature study. The comparison revealed that GMDH-type NN was a better approach for forecasting the monthly electricity demand of Turkey. Finally, the developed model was utilized to forecast monthly electricity demand in the period of 2019/12-2020/12.

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Cite This Article

AKKAYA, A. V. (2022). GMDH-type neural network-based monthly electricity demand forecasting of Turkey. International Advanced Researches and Engineering Journal, Cilt: 5 - Sayı: 1, 53–60. https://ajindex.com/articles/539317

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References (16)

  1. Tutun S, Chou C-A, Canıyılmaz E, A new forecasting framework for volatile behavior in net electricity consumption: A case study in Turkey. Energy, 2015. 93: p. 2406-2422
  2. Cunkas M, Taskiran U, Turkey’s electricity consumption forecasting using genetic programming. Energy Sources, Part B: Economics, Planning, and Policy, 2011. 6: p. 406–416
  3. Toksari M. D., A hybrid algorithm of ant colony optimization (ACO) and iterated local search (ILS) for estimating electricity domestic consumption: Case of Turkey. Electrical Power and Energy Systems, 2016. 78: p. 776–782
  4. Günay ME., Forecasting annual gross electricity demand by artificial neural networks using predicted values of socio-economic indicator sand climatic conditions: Case of Turkey. Energy Policy, 2016. 90: p. 92–101
  5. Hamzacebi C, Es H.A., Forecasting the annual electricity consumption of Turkey using an optimized grey model. Energy, 2014. 70: p. 165-171
  6. Kavaklioglu K, Ceylan H, Ozturk H.K, Canyurt O.E., Modeling and prediction of Turkey’s electricity consumption using Artificial Neural Networks. Energy Conversion and Management, 2009. 50: p. 2719–2727
  7. Kıran MS, Ozceylan E, Gunduz M, Paksoy T. Swarm intelligence approaches to estimate electricity energy demand in Turkey. Knowledge-Based Systems, 2012. 36: p. 93–103
  8. İlseven E, and Göl M., Medium-term electricity demand forecasting based on MARS, in IEEE PES Innovative Smart Grid Technologies Conference Europe (ISGT-Europe) 2017: Torino, Sept 26-29
  9. Hong T, Fan S., Probabilistic electric load forecasting: A tutorial review. International Journal of Forecasting, 2016. 32: p. 914-938
  10. Yumurtaci Z, Asmaz E., Electric energy demand of Turkey for the year 2050. Energy Source, 2004. 26: p. 1157-1164
  11. Erdogdu E., Electricity demand analysis using cointegration and ARIMA modelling: a case study of Turkey. Energy Policy, 2007. 35: p. 1129-1146
  12. Dilaver Z, Hunt LC., Turkish aggregate electricity demand: an outlook to 2020. Energy, 2011. 36: p. 6686-6696
  13. Hamzacebi C., Improving artificial neural networks’ performance in seasonal time series forecasting. Information Sciences, 2008. 178: p.4550–4559
  14. Saab S, Badr E, Nasr G., Univariate modeling and forecasting of energy consumption: the case of electricity in Lebanon. Energy, 2001. 26(1): p.1–14
  15. Bianco V, Manca O, Nardini S., Linear regression models to forecast electricity consumption in Italy. Energy Sour B Econ Plan Policy, 2013. 8(1): p.86–93
  16. Pao H.T. 2006