Research Article
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Year 2025, Volume: 9 Issue: 1, 89 - 102, 17.06.2025

Mehmet Ali Arslan , Tarık Talan

Abstract

References

  • 1. Yılankırkan, N., & Doğan, H. (2020). Türkiye’nin enerji görünümü ve 2023 yılı birincil enerji arz projeksiyonu. Batman Üniversitesi Yaşam Bilimleri Dergisi, 10(2), 77-92.
  • 2. Kızıldere, C. (2020). Türkiye’de cari açık sorununun enerji tüketimi ve ekonomik büyüme açısından değerlendirilmesi: Ampirik bir analiz. Business & Management Studies: An International Journal, 8(2), 2121-2139. http://dx.doi.org/10.15295/bmij.v8i2.1493
  • 3. EİGM Raporları—T.C. Enerji ve Tabii Kaynaklar Bakanlığı [- Republic of Türkiye Ministry of Energy and Natural Resources]. (2024). https://enerji.gov.tr/eigm-raporlari.
  • 4. Karaman, Ö. A., & Bektaş, Y. (2023). Makine öğrenmesi ve optimizasyon yöntemleri ile uzun dönem elektrik enerjisi tahmini: Türkiye örneği. Mühendislik Bilimleri ve Araştırmaları Dergisi, 5(2), 285-292. https://doi.org/10.46387/bjesr.1306577
  • 5. Ekinci, F. (2019). YSA VE ANFIS tekniklerine dayalı enerji tüketim tahmin yöntemlerinin karşılaştırılması. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 7(3), 1029-1044. https://doi.org/10.29130/dubited.485822
  • 6. Özden, S., & Öztürk, A. (2018). Yapay sinir ağları ve zaman serileri yöntemi ile bir endüstri alanının (ivedik OSB) elektrik enerjisi ihtiyaç tahmini. Bilişim Teknolojileri Dergisi, 11(3), 255-261. https://doi.org/10.17671/gazibtd.404250
  • 7. Zeng, B., Tan, Y., Xu, H., Quan, J., Wang, L., & Zhou, X. (2018). Forecasting the electricity consumption of commercial sector in hong kong using a novel grey dynamic prediction model. Journal of Grey System, 30(1), 159-174.
  • 8. Pençe, İ., Kalkan, A., & Çeşmeli, M. Ş. (2019). Türkiye sanayi elektrik enerjisi tüketiminin 2017-2023 dönemi için yapay sinir ağları ile tahmini. Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi, 3(2), 206-228. https://doi.org/10.31200/makuubd.538878
  • 9. Işık, H., & Şeker, M. (2021). Yapay Sinir Ağı (YSA) kullanarak farklı kaynaklardan Türkiye’de elektrik enerjisi üretim potansiyelinin tahmini. Computer Science, Special, 304-311. https://doi.org/10.53070/bbd.991039
  • 10. Lee, M. H. L., Ser, Y. C., Selvachandran, G., Thong, P. H., Cuong, L., Son, L. H., Tuan, N. T., & Gerogiannis, V. C. (2022). A comparative study of forecasting electricity consumption using machine learning models. Mathematics, 10(8), 1329. https://doi.org/10.3390/math10081329
  • 11. Tarmanini, C., Sarma, N., Gezegin, C., & Ozgonenel, O. (2023). Short term load forecasting based on ARIMA and ANN approaches. Energy Reports, 9, 550-557. https://doi.org/10.1016/j.egyr.2023.01.060
  • 12. Lazzari, F., Mor, G., Cipriano, J., Gabaldon, E., Grillone, B., Chemisana, D., & Solsona, F. (2022). User behaviour models to forecast electricity consumption of residential customers based on smart metering data. Energy Reports, 8, 3680-3691. https://doi.org/10.1016/j.egyr.2022.02.260
  • 13. Ramos, D., Faria, P., Vale, Z., Mourinho, J., & Correia, R. (2020). Industrial facility electricity consumption forecast using artificial neural networks and incremental learning. Energies, 13(18), 4774. https://doi.org/10.3390/en13184774
  • 14. Pala, Z. (2023). Prediction of electricity consumption in Türkiye with time series. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 4(1), 32-40.
  • 15. Hamzaçebi, C. (2007). Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy policy, 35(3), 2009-2016. https://doi.org/10.1016/j.enpol.2006.03.014
  • 16. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control. John Wiley & Sons. https://doi.org/10.1002/9781118619193
  • 17. Hyndman, R. J. (2018). Forecasting: Principles and practice. 2nd ed. Melbourne: OTexts.
  • 18. Assimakopoulos, V., & Nikolopoulos, K. (2000). The theta model: A decomposition approach to forecasting. International journal of forecasting, 16(4), 521-530. https://doi.org/10.1016/S0169-2070(00)00066-2
  • 19. Özoğuz, K. (1986). Zaman serilerinde trend fonksiyon tipinin belirlenmesi ve yorumu. İstanbul Üniversitesi İktisat Fakültesi Mecmuası, 42(1-4).
  • 20. De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496), 1513-1527. https://doi.org/10.1198/jasa.2011.tm09771
  • 21. Hyndman, R., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with exponential smoothing: The state space approach. Springer Science & Business Media.
  • 22. Kaynar, O., & Taştan, S. (2009). Zaman serisi analizinde MLP yapay sinir ağları ve ARIMA modelinin karşılaştırılması. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 33, 161-172.
  • 23. Pinkus, A. (1999). Approximation theory of the MLP model in neural networks. Acta numerica, 8, 143-195. https://doi.org/10.1017/S0962492900002919
  • 24. Saigal, S., & Mehrotra, D. (2012). Performance comparison of time series data using predictive data mining techniques. Advances in Information Mining, 4(1), 57-66.
  • 25. Qureshi, M., Arbab, M.A. & Rehman, S. (2024). Deep learning-based forecasting of electricity consumption. Sci Rep 14, 6489. https://doi.org/10.1038/s41598-024-56602-4
  • 26. Liu, X., Li, S., & Gao, M. (2024). A discrete time-varying grey Fourier model with fractional order terms for electricity consumption forecast. Energy, 296, 131065. https://doi.org/10.1016/j.energy.2024.131065
  • 27. Peteleaza, D., Matei, A., Sorostinean, R., Gellert, A., Fiore, U., Zamfirescu, B. C., & Palmieri, F. (2024). Electricity consumption forecasting for sustainable smart cities using machine learning methods. Internet of Things, 27, 101322. https://doi.org/10.1016/j.iot.2024.101322
  • 28. Matos, M., Almeida, J., Gonçalves, P., Baldo, F., Braz, F. J., & Bartolomeu, P. C. (2024). A machine learning-based electricity consumption forecast and management system for renewable energy communities. Energies, 17(3), 630. https://doi.org/10.3390/en17030630
  • 29. Kim, Y. S., Kim, M. K., Fu, N., Liu, J., Wang, J., & Srebric, J. (2025). Investigating the impact of data normalization methods on predicting electricity consumption in a building using different artificial neural network models. Sustainable Cities and Society, 118, 105570. https://doi.org/10.1016/j.scs.2024.105570
  • 30. Leite Coelho da Silva, F., da Silva Cordeiro, J., da Costa, K., Saboya, N., Canas Rodrigues, P., & López-Gonzales, J. L. (2025). Time series forecasting via integrating a filtering method: an application to electricity consumption. Comput Stat. https://doi.org/10.1007/s00180-024-01595-x
  • 31. Nazir, M.U., Li, J. (2025). Forecasting of electricity consumption in Pakistan based on integrating machine learning algorithms and Monte Carlo simulation. Electr Eng. https://doi.org/10.1007/s00202-024-02923-6
  • 32. Zhang, X., Dang, Y., Ding, S., Wang, H., & Ding, F. (2025). Multi-output discrete grey model tailored for electricity consumption forecast. Applied Mathematical Modelling, 139, 115822. https://doi.org/10.1016/j.apm.2024.115822
  • 33. Mahia, F., Dey, A. R., Masud, M. A., and Mahmud, M. S. (2019). Forecasting electricity consumption using ARIMA model. 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), Dhaka, Bangladesh, pp. 1-6, https://doi.org/10.1109/STI47673.2019.9068076

Comparative Analysis of Electricity Consumption Forecast

Year 2025, Volume: 9 Issue: 1, 89 - 102, 17.06.2025

Mehmet Ali Arslan , Tarık Talan

Abstract

This study aims to make a comparative analysis of electricity consumption forecast using artificial intelligence (AI) and statistical models. In order to reduce the current deficits of countries, it is of great importance to predict the future electricity consumption amount and plan the power plant capacities accordingly. Electricity is an energy source that is extremely difficult to store when used in sectors such as industry and housing. Therefore, the electricity produced must be consumed immediately without causing energy losses and waste. In this context, ensuring the balance between electricity production and consumption can correctly contribute to the management of the current deficit by increasing economic efficiency. In the current study, Türkiye's hourly electricity consumption data between 2016 and 2024 were examined. These data were transformed into a 108-month consumption data set. Seven different models, namely Auto-ARIMA, Holt-Winters, Theta, ETS, TBATS, NNETAR and MLP, were used in the analyses. Among the models, NNETAR and MLP are AI based, and the others are statistical-based models. In this way, the effectiveness of different model types in electricity consumption estimations was compared. In this study, the Auto-ARIMA model stood out with a 3.77% MAPE error rate. When such studies are considered within the framework of countries' energy policies, they can make a significant contribution to reducing the current deficit of the country's economy. As a result of the study, it was concluded that the Auto-ARIMA model should be taken into consideration when making estimates on how many Megawatt power plants should be built in order to meet future energy needs in shaping energy policies in Türkiye.

Keywords

Electrical Energy Electricity production forecast Artificial intelligence Deep learning

References

  • 1. Yılankırkan, N., & Doğan, H. (2020). Türkiye’nin enerji görünümü ve 2023 yılı birincil enerji arz projeksiyonu. Batman Üniversitesi Yaşam Bilimleri Dergisi, 10(2), 77-92.
  • 2. Kızıldere, C. (2020). Türkiye’de cari açık sorununun enerji tüketimi ve ekonomik büyüme açısından değerlendirilmesi: Ampirik bir analiz. Business & Management Studies: An International Journal, 8(2), 2121-2139. http://dx.doi.org/10.15295/bmij.v8i2.1493
  • 3. EİGM Raporları—T.C. Enerji ve Tabii Kaynaklar Bakanlığı [- Republic of Türkiye Ministry of Energy and Natural Resources]. (2024). https://enerji.gov.tr/eigm-raporlari.
  • 4. Karaman, Ö. A., & Bektaş, Y. (2023). Makine öğrenmesi ve optimizasyon yöntemleri ile uzun dönem elektrik enerjisi tahmini: Türkiye örneği. Mühendislik Bilimleri ve Araştırmaları Dergisi, 5(2), 285-292. https://doi.org/10.46387/bjesr.1306577
  • 5. Ekinci, F. (2019). YSA VE ANFIS tekniklerine dayalı enerji tüketim tahmin yöntemlerinin karşılaştırılması. Düzce Üniversitesi Bilim ve Teknoloji Dergisi, 7(3), 1029-1044. https://doi.org/10.29130/dubited.485822
  • 6. Özden, S., & Öztürk, A. (2018). Yapay sinir ağları ve zaman serileri yöntemi ile bir endüstri alanının (ivedik OSB) elektrik enerjisi ihtiyaç tahmini. Bilişim Teknolojileri Dergisi, 11(3), 255-261. https://doi.org/10.17671/gazibtd.404250
  • 7. Zeng, B., Tan, Y., Xu, H., Quan, J., Wang, L., & Zhou, X. (2018). Forecasting the electricity consumption of commercial sector in hong kong using a novel grey dynamic prediction model. Journal of Grey System, 30(1), 159-174.
  • 8. Pençe, İ., Kalkan, A., & Çeşmeli, M. Ş. (2019). Türkiye sanayi elektrik enerjisi tüketiminin 2017-2023 dönemi için yapay sinir ağları ile tahmini. Mehmet Akif Ersoy Üniversitesi Uygulamalı Bilimler Dergisi, 3(2), 206-228. https://doi.org/10.31200/makuubd.538878
  • 9. Işık, H., & Şeker, M. (2021). Yapay Sinir Ağı (YSA) kullanarak farklı kaynaklardan Türkiye’de elektrik enerjisi üretim potansiyelinin tahmini. Computer Science, Special, 304-311. https://doi.org/10.53070/bbd.991039
  • 10. Lee, M. H. L., Ser, Y. C., Selvachandran, G., Thong, P. H., Cuong, L., Son, L. H., Tuan, N. T., & Gerogiannis, V. C. (2022). A comparative study of forecasting electricity consumption using machine learning models. Mathematics, 10(8), 1329. https://doi.org/10.3390/math10081329
  • 11. Tarmanini, C., Sarma, N., Gezegin, C., & Ozgonenel, O. (2023). Short term load forecasting based on ARIMA and ANN approaches. Energy Reports, 9, 550-557. https://doi.org/10.1016/j.egyr.2023.01.060
  • 12. Lazzari, F., Mor, G., Cipriano, J., Gabaldon, E., Grillone, B., Chemisana, D., & Solsona, F. (2022). User behaviour models to forecast electricity consumption of residential customers based on smart metering data. Energy Reports, 8, 3680-3691. https://doi.org/10.1016/j.egyr.2022.02.260
  • 13. Ramos, D., Faria, P., Vale, Z., Mourinho, J., & Correia, R. (2020). Industrial facility electricity consumption forecast using artificial neural networks and incremental learning. Energies, 13(18), 4774. https://doi.org/10.3390/en13184774
  • 14. Pala, Z. (2023). Prediction of electricity consumption in Türkiye with time series. Muş Alparslan Üniversitesi Mühendislik Mimarlık Fakültesi Dergisi, 4(1), 32-40.
  • 15. Hamzaçebi, C. (2007). Forecasting of Turkey’s net electricity energy consumption on sectoral bases. Energy policy, 35(3), 2009-2016. https://doi.org/10.1016/j.enpol.2006.03.014
  • 16. Box, G. E., Jenkins, G. M., Reinsel, G. C., & Ljung, G. M. (2015). Time series analysis: Forecasting and control. John Wiley & Sons. https://doi.org/10.1002/9781118619193
  • 17. Hyndman, R. J. (2018). Forecasting: Principles and practice. 2nd ed. Melbourne: OTexts.
  • 18. Assimakopoulos, V., & Nikolopoulos, K. (2000). The theta model: A decomposition approach to forecasting. International journal of forecasting, 16(4), 521-530. https://doi.org/10.1016/S0169-2070(00)00066-2
  • 19. Özoğuz, K. (1986). Zaman serilerinde trend fonksiyon tipinin belirlenmesi ve yorumu. İstanbul Üniversitesi İktisat Fakültesi Mecmuası, 42(1-4).
  • 20. De Livera, A. M., Hyndman, R. J., & Snyder, R. D. (2011). Forecasting time series with complex seasonal patterns using exponential smoothing. Journal of the American Statistical Association, 106(496), 1513-1527. https://doi.org/10.1198/jasa.2011.tm09771
  • 21. Hyndman, R., Koehler, A. B., Ord, J. K., & Snyder, R. D. (2008). Forecasting with exponential smoothing: The state space approach. Springer Science & Business Media.
  • 22. Kaynar, O., & Taştan, S. (2009). Zaman serisi analizinde MLP yapay sinir ağları ve ARIMA modelinin karşılaştırılması. Erciyes Üniversitesi İktisadi ve İdari Bilimler Fakültesi Dergisi, 33, 161-172.
  • 23. Pinkus, A. (1999). Approximation theory of the MLP model in neural networks. Acta numerica, 8, 143-195. https://doi.org/10.1017/S0962492900002919
  • 24. Saigal, S., & Mehrotra, D. (2012). Performance comparison of time series data using predictive data mining techniques. Advances in Information Mining, 4(1), 57-66.
  • 25. Qureshi, M., Arbab, M.A. & Rehman, S. (2024). Deep learning-based forecasting of electricity consumption. Sci Rep 14, 6489. https://doi.org/10.1038/s41598-024-56602-4
  • 26. Liu, X., Li, S., & Gao, M. (2024). A discrete time-varying grey Fourier model with fractional order terms for electricity consumption forecast. Energy, 296, 131065. https://doi.org/10.1016/j.energy.2024.131065
  • 27. Peteleaza, D., Matei, A., Sorostinean, R., Gellert, A., Fiore, U., Zamfirescu, B. C., & Palmieri, F. (2024). Electricity consumption forecasting for sustainable smart cities using machine learning methods. Internet of Things, 27, 101322. https://doi.org/10.1016/j.iot.2024.101322
  • 28. Matos, M., Almeida, J., Gonçalves, P., Baldo, F., Braz, F. J., & Bartolomeu, P. C. (2024). A machine learning-based electricity consumption forecast and management system for renewable energy communities. Energies, 17(3), 630. https://doi.org/10.3390/en17030630
  • 29. Kim, Y. S., Kim, M. K., Fu, N., Liu, J., Wang, J., & Srebric, J. (2025). Investigating the impact of data normalization methods on predicting electricity consumption in a building using different artificial neural network models. Sustainable Cities and Society, 118, 105570. https://doi.org/10.1016/j.scs.2024.105570
  • 30. Leite Coelho da Silva, F., da Silva Cordeiro, J., da Costa, K., Saboya, N., Canas Rodrigues, P., & López-Gonzales, J. L. (2025). Time series forecasting via integrating a filtering method: an application to electricity consumption. Comput Stat. https://doi.org/10.1007/s00180-024-01595-x
  • 31. Nazir, M.U., Li, J. (2025). Forecasting of electricity consumption in Pakistan based on integrating machine learning algorithms and Monte Carlo simulation. Electr Eng. https://doi.org/10.1007/s00202-024-02923-6
  • 32. Zhang, X., Dang, Y., Ding, S., Wang, H., & Ding, F. (2025). Multi-output discrete grey model tailored for electricity consumption forecast. Applied Mathematical Modelling, 139, 115822. https://doi.org/10.1016/j.apm.2024.115822
  • 33. Mahia, F., Dey, A. R., Masud, M. A., and Mahmud, M. S. (2019). Forecasting electricity consumption using ARIMA model. 2019 International Conference on Sustainable Technologies for Industry 4.0 (STI), Dhaka, Bangladesh, pp. 1-6, https://doi.org/10.1109/STI47673.2019.9068076
There are 33 citations in total.

Details

Primary Language English
Subjects Artificial Intelligence (Other)
Journal Section Research Articles
Authors

Mehmet Ali Arslan 0000-0002-0701-059X

Tarık Talan 0000-0002-5371-4520

Early Pub Date June 11, 2025
Publication Date June 17, 2025
Submission Date January 14, 2025
Acceptance Date April 17, 2025
Published in Issue Year 2025Volume: 9 Issue: 1

Cite

APA Arslan, M. A., & Talan, T. (2025). Comparative Analysis of Electricity Consumption Forecast. Journal of Innovative Science and Engineering, 9(1), 89-102. https://doi.org/10.38088/jise.1619782
AMA Arslan MA, Talan T. Comparative Analysis of Electricity Consumption Forecast. JISE. June 2025;9(1):89-102. doi:10.38088/jise.1619782
Chicago Arslan, Mehmet Ali, and Tarık Talan. “Comparative Analysis of Electricity Consumption Forecast”. Journal of Innovative Science and Engineering 9, no. 1 (June 2025): 89-102. https://doi.org/10.38088/jise.1619782.
EndNote Arslan MA, Talan T (June 1, 2025) Comparative Analysis of Electricity Consumption Forecast. Journal of Innovative Science and Engineering 9 1 89–102.
IEEE M. A. Arslan and T. Talan, “Comparative Analysis of Electricity Consumption Forecast”, JISE, vol. 9, no. 1, pp. 89–102, 2025, doi: 10.38088/jise.1619782.
ISNAD Arslan, Mehmet Ali - Talan, Tarık. “Comparative Analysis of Electricity Consumption Forecast”. Journal of Innovative Science and Engineering 9/1 (June 2025), 89-102. https://doi.org/10.38088/jise.1619782.
JAMA Arslan MA, Talan T. Comparative Analysis of Electricity Consumption Forecast. JISE. 2025;9:89–102.
MLA Arslan, Mehmet Ali and Tarık Talan. “Comparative Analysis of Electricity Consumption Forecast”. Journal of Innovative Science and Engineering, vol. 9, no. 1, 2025, pp. 89-102, doi:10.38088/jise.1619782.
Vancouver Arslan MA, Talan T. Comparative Analysis of Electricity Consumption Forecast. JISE. 2025;9(1):89-102.
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