Forecasting electricity consumption: A comparison of regression analysis, neural networks and least squares support vector machines

Kaytez F., TAPLAMACIOĞLU M. C., ÇAM E., HARDALAÇ F.

INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS, cilt.67, ss.431-438, 2015 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 67
  • Basım Tarihi: 2015
  • Doi Numarası: 10.1016/j.ijepes.2014.12.036
  • Dergi Adı: INTERNATIONAL JOURNAL OF ELECTRICAL POWER & ENERGY SYSTEMS
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus
  • Sayfa Sayıları: ss.431-438
  • Anahtar Kelimeler: Electricity consumption forecasting, Regression analysis, Artificial neural network, Least square support vector machines, ENERGY DEMAND, ECONOMIC-GROWTH, TURKEY, PREDICTION, ALGORITHM, TAIWAN, MARKET, GDP
  • Gazi Üniversitesi Adresli: Evet

Özet

Accurate electricity consumption forecast has primary importance in the energy planning of the developing countries. During the last decade several new techniques are being used for electricity consumption planning to accurately predict the future electricity consumption needs. Support vector machines (SVMs) and least squares support vector machines (LS-SVMs) are new techniques being adopted for energy consumption forecasting. In this study, the LS-SVM is implemented for the prediction of electricity energy consumption of Turkey. In addition to the traditional regression analysis and artificial neural networks (ANNs) are considered. In the models, gross electricity generation, installed capacity, total subscribership and population are used as independent variables using historical data from 1970 to 2009. Forecasting results are compared using diverse performance criteria in this study with each other. Receiver operating characteristic (ROC) analysis is realized for determining the specificity and sensitivity of the empirical results. The results indicate that the proposed LS-SVM model is an accurate and a quick prediction method. (C) 2014 Elsevier Ltd. All rights reserved.