Electric energy demands of Turkey in residential and industrial sectors

BİLGİLİ M., ŞAHİN B., YAŞAR A., ŞİMŞEK E.

RENEWABLE & SUSTAINABLE ENERGY REVIEWS, cilt.16, sa.1, ss.404-414, 2012 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 16 Sayı: 1
  • Basım Tarihi: 2012
  • Doi Numarası: 10.1016/j.rser.2011.08.005
  • Dergi Adı: RENEWABLE & SUSTAINABLE ENERGY REVIEWS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.404-414
  • Anahtar Kelimeler: Artificial neural networks (ANNs), Electricity consumption, Industrial sector, Linear regression (LR), Nonlinear regression (NLR), Residential sector, ARTIFICIAL NEURAL-NETWORKS, GENETIC ALGORITHM APPROACH, RENEWABLE ENERGY, ENVIRONMENTAL-POLLUTION, EXERGY PRODUCTION, WIND-SPEED, CONSUMPTION, PREDICTION, HYDROPOWER, POLICIES
  • Çukurova Üniversitesi Adresli: Evet

Özet

The main objective of the present study is to apply the artificial neural network (ANN) methodology, linear regression (LR) and nonlinear regression (NLR) models to estimate the electricity consumptions of the residential and industrial sectors in Turkey. Installed capacity, gross electricity generation, population and total subscribership were selected as independent variables. Two different scenarios (powerful and poor) were proposed for prediction of the future electricity consumption. Obtained results of the LR, NLR and ANN models were also compared with each other as well as the projection of the Ministry of Energy and Natural Resources (MENR) and the results in literature. Results of the comparison showed that the performance values of the ANN method are better than the performance values of the LR and NLR models. According to the poor scenario and ANN model, Turkey's residential and industrial sector electricity consumptions will increase to value of 140.64 TWh and 124.85 TWh by 2015, respectively. (C) 2011 Elsevier Ltd. All rights reserved.