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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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