Please use this identifier to cite or link to this item: https://hdl.handle.net/11499/6133
Title: Modeling and prediction of Turkey's electricity consumption using Support Vector Regression
Authors: Kavaklıoğlu, Kadir
Keywords: Electricity consumption
Energy modeling
Prediction
Support Vector Regression
Time series
Turkey
Economics
Forecasting
Mean square error
Regression analysis
Vectors
Electricity-consumption
Energy model
Gross national product
Modeling and predictions
Root mean square errors
Socio-economic indicators
Support vector regression (SVR)
Electric power utilization
electrical power
fuel consumption
future prospect
prediction
socioeconomic indicator
time series
Publisher: Elsevier Ltd
Abstract: Support Vector Regression (SVR) methodology is used to model and predict Turkey's electricity consumption. Among various SVR formalisms, ?-SVR method was used since the training pattern set was relatively small. Electricity consumption is modeled as a function of socio-economic indicators such as population, Gross National Product, imports and exports. In order to facilitate future predictions of electricity consumption, a separate SVR model was created for each of the input variables using their current and past values; and these models were combined to yield consumption prediction values. A grid search for the model parameters was performed to find the best ?-SVR model for each variable based on Root Mean Square Error. Electricity consumption of Turkey is predicted until 2026 using data from 1975 to 2006. The results show that electricity consumption can be modeled using Support Vector Regression and the models can be used to predict future electricity consumption. © 2010 Elsevier Ltd.
URI: https://hdl.handle.net/11499/6133
https://doi.org/10.1016/j.apenergy.2010.07.021
ISSN: 0306-2619
Appears in Collections:Mühendislik Fakültesi Koleksiyonu
Scopus İndeksli Yayınlar Koleksiyonu / Scopus Indexed Publications Collection
WoS İndeksli Yayınlar Koleksiyonu / WoS Indexed Publications Collection

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