Forecasting of Turkey's monthly electricity demand by seasonal artificial neural network

Hamzaçebi C., Es H. A., Cakmak R.

NEURAL COMPUTING & APPLICATIONS, cilt.31, ss.2217-2231, 2019 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 31
  • Basım Tarihi: 2019
  • Doi Numarası: 10.1007/s00521-017-3183-5
  • Dergi Adı: NEURAL COMPUTING & APPLICATIONS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.2217-2231
  • Anahtar Kelimeler: Artificial neural network, Seasonal electricity demand, SARIMA, forecasting, Turkey, ENERGY-CONSUMPTION, SHORT-TERM, ECONOMIC-GROWTH, LOAD, PREDICTION, REGRESSION, VECTOR, MODELS, COINTEGRATION, PERFORMANCE
  • Karadeniz Teknik Üniversitesi Adresli: Evet

Özet

Electricity is one of the most important end-user energy types in today's world and has an effective role in development of societies and economies. Stability of electricity supply is provided by matching of generated and consumed electricity amount during the all-day. So, electricity consumption forecasting is an essential issue for electric utilities. In this study, the monthly electricity demand of Turkey has been predicted. To model the effects of seasonality and trend, four different ANN models have been developed and selected the superior one. In addition, the selected ANN model has been compared with SARIMA model in order to increase the acceptability and reliability of the ANN model. The monthly electricity demand of Turkey has been predicted between 2015 and 2018 via the ANN model that can make successful and high-accuracy predictions according to the performance measures. The forecasting values will help in determining the medium-term and stable energy policies.