Turkey's Long-Term Electricity Consumption Forecast


Emec S., AKKAYA G.

JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH, cilt.81, sa.12, ss.1336-1341, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 81 Sayı: 12
  • Basım Tarihi: 2022
  • Doi Numarası: 10.56042/jsir.v81i12.40731
  • Dergi Adı: JOURNAL OF SCIENTIFIC & INDUSTRIAL RESEARCH
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, INSPEC, Directory of Open Access Journals
  • Sayfa Sayıları: ss.1336-1341
  • Anahtar Kelimeler: Demand estimation, Electricity demand, Forecasting, Regression analysis, ENERGY-CONSUMPTION, NEURAL-NETWORK, PREDICTION, DEMAND, PROJECTIONS, ALGORITHM
  • Atatürk Üniversitesi Adresli: Evet

Özet

Demand forecasting is essential primarily for planning. Although it is crucial in many sectors and issues, it has particular importance for electricity. Therefore, the issue of electricity consumption forecasting has recently become a prevalent topic. In light of the above, this study aimed to develop an appropriate model to estimate the long-term electricity consumption of Turkey. The study consists of three steps. In the first step, eight models were developed to separately investigate the effects of eight input variables frequently used in electricity consumption forecasting studies in the literature. In the second step of the study, two models consisting of input variables with high impact in the first step were developed, and the trained performances of the developed models were calculated by using the regression analysis. In the final step, the combined effect of eight variables on electricity consumption forecasting was investigated using regression analysis. It can be conclude that the model in the third step showed significant results, and the model performance was good. Finally, Turkey's electricity consumption forecast for the years 2020-2030 was performed using the model in the third step.