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

Year 2021, Volume: 5 Issue: 1, 53 - 60, 15.04.2021

Ali Volkan Akkaya

https://doi.org/10.35860/iarej.766762

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.

Keywords

Electricity demand, Forecasting, GMDH neural network, Modeling

References

• 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. Comparing linear and nonlinear forecasts for Taiwan’s electricity consumption. Energy 31(12):2129–2141.
• 17. Çunkaş M, Altun A.A., Long term electricity demand forecasting in Turkey using artificial neural networks. Energy Sources, Part B: Economics, Planning, and Policy, 2010. 5(3): p. 279-289.
• 18. Kucukali S, Baris K., Turkeys short-term gross annual electricity demand forecast by fuzzy logic approach. Energy Policy, 2010. 38(5): p. 2438-2445.
• 19. Kavaklioglu K., Modeling and prediction of Turkey’s electricity consumption using support vector regression. Applied Energy, 2011. 88: p. 368-375.
• 20. Sözen A, Isikan O, Menlik T, Arcaklioglu E., The Forecasting of Net Electricity Consumption of the Consumer Groups in Turkey. Energy Sources, Part B: Economics, Planning, and Policy, 2011. 6(1): p. 20-46.
• 21. Assareh E, Behrang MA, Ghanbarzdeh A., Forecasting energy demand in Iran using Genetic Algorithm (GA) and Particle Swarm Optimization (PSO) methods. Energy Sources, Part B: Economics, Planning, and Policy, 2012. 7(4): p.411-422.
• 22. Ayvaz B, Kusakci A.O., Electricity consumption forecasting for Turkey with nonhomogeneous discrete grey model. Energy Sources, Part B: Economics, Planning, and Policy, 2017. 12(3): p. 260-267.
• 23. Bilgili M, Sahin B, Yasar A, Simsek E., Electric energy demands of Turkey in residential and industrial sectors. Renewable and Sustainable Energy Reviews, 2012. 16(1): p. 404–414.
• 24. Chen JF, Lo SK, Do QH.,Forecasting monthly electricity demands: an application of neural networks trained by heuristic algorithms. Information, 2017. 8(1): 31.
• 25. Uzlu E., Application of Jaya algorithm-trained artificial neural networks for prediction of energy use in the nation of Turkey. Energy Sources, Part B: Economics, Planning, and Policy, 2019. 14(5): p. 183-200.
• 26. Hamzacebi C, Es HA, Cakmak R., Forecasting of Turkey’s monthly electricity demand by seasonal artificial neural network. Neural Computing and Applications, 2019. 31: p. 2217–2231.
• 27. Luzar M, Sobolewski Ł, Miczulski W., Korbicz J., Prediction of corrections for the Polish time scale UTC(PL) using artificial neural networks. Bulletin of the Polish Academy of Sciences Technical Sciences, 2013. 61(3): p. 589-594.
• 28. Amjady N, Daraeepour A., Midterm Demand Prediction of Electrical Power Systems Using a New Hybrid Forecast Technique. IEEE Transactions on Power Systems, 2011. 26(2): p. 755-765.
• 29. Ivakhnenko AG., Polynomial theory of complex systems, IEEE Transactions on Systems, Man and Cybernetics, 1971. 1(4): p. 364–378.
• 30. Ahmadi MA, Golshadi M., Neural network based swarm concept for prediction asphaltene precipitation due to natural depletion. Journal of Petroleum Science and Engineering, 2012. 98: p. 40–49.
• 31. Ahmadi MH., Ahmadi MA, Mehrpooya M, Rosen MA., Using GMDH neural networks to model the power and torque of a Stirling engine. Sustainability, 2015. 7 (2): p. 2243–2255.
• 32. Nariman-Zadeh, N, Darvizeh A, Felezi ME, and Gharababaei H., Polynomial modelling of explosive compaction process of metallic powders using GMDH-type neural networks and singular value decomposition. Modelling and Simulation in Materials Science and Engineering, 2002. 10 (6): 727.
• 33. Rezaei MH, Sadeghzadeh M, Nazari MA, Ahmadi MH, Astaraei FR., Applying GMDH artificial neural network in modeling CO2 emissions in four Nordic countries, International Journal of Low-Carbon Technologies, 2018. 13: p. 266–271.
• 34. Jia X, Di Y, Feng J, Yang Q, Dai H, Lee J., Adaptive virtual metrology for semiconductor chemical mechanical planarization process using GMDH-type polynomial neural networks. Journal of Process Control, 2018. 62: p. 44–54.
• 35. Zhou L., Prediction of CO2 adsorption on different activated carbons by hybrid group method of data-handling networks and LSSVM, Energy Sources, Part A: Recovery, Utilization, and Environmental Effects, 2019. 41 (16): p. 1960–1971.
• 36. Li RYM, Fong S, Chong KWS., Forecasting the REITs and stock indices: Group Method of Data Handling Neural Network Approach. Pacific Rim Property Research Journal, 2017. 23(2): p. 123–160.
• 37. Turkish Electricity Transmission Cooperation (TEIAS). 2019. Electricity Generation Transmission Statistics of Turkey. [cited 2019, 21 December]; Available from: http://www.teias.gov.tr.