| dc.contributor.author | Oğuz Kaynar | |
| dc.contributor.author | Ferhan Demirkoparan | |
| dc.contributor.author | Halil Özekicioğlu | |
| dc.date.accessioned | 23.07.201910:49:13 | |
| dc.date.accessioned | 2019-07-23T16:37:51Z | |
| dc.date.available | 23.07.201910:49:13 | |
| dc.date.available | 2019-07-23T16:37:51Z | |
| dc.date.issued | 2017 | |
| dc.identifier.issn | 1304-5318 | |
| dc.identifier.uri | http://www.trdizin.gov.tr/publication/paper/detail/TWpjM016azRPQT09 | |
| dc.identifier.uri | https://hdl.handle.net/20.500.12418/3542 | |
| dc.description.abstract | Energy is a very important factor in terms of sustaining the economic development for developing and industrialized countries. Electricity is one of the most important forms of energy for industrialization and improvement of living standards. The estimation and modeling of electricity consumption has a special importance in Turkey which is a foreign-dependent country in energy. In this study, a forecasting application is made by using Turkey’s electricity consumption, population, import, export and gross domestic product between 1975-2014 employing support vector regression methods. Chaotic particle swarm optimization algorithm (CPSO) is used to choose the parameters of SVR | en_US |
| dc.description.abstract | Energy is a very important factor in terms of sustaining the economic development for developing and industrialized countries. Electricity is one of the most important forms of energy for industrialization and improvement of living standards. The estimation and modeling of electricity consumption has a special importance in Turkey which is a foreign-dependent country in energy. In this study, a forecasting application is made by using Turkey’s electricity consumption, population, import, export and gross domestic product between 1975-2014 employing support vector regression methods. Chaotic particle swarm optimization algorithm (CPSO) is used to choose the parameters of SVR | en_US |
| dc.language.iso | eng | en_US |
| dc.rights | info:eu-repo/semantics/openAccess | en_US |
| dc.subject | İktisat | en_US |
| dc.subject | İşletme | en_US |
| dc.title | Forecasting of Turkey’s Electricity Consumption with Support Vector Regression and Chaotic Particle Swarm Algorithm | en_US |
| dc.type | article | en_US |
| dc.relation.journal | Yönetim Bilimleri Dergisi | en_US |
| dc.contributor.department | Sivas Cumhuriyet Üniversitesi | en_US |
| dc.identifier.volume | 15 | en_US |
| dc.identifier.issue | 29 | en_US |
| dc.identifier.endpage | 224 | en_US |
| dc.identifier.startpage | 211 | en_US |
| dc.relation.publicationcategory | Makale - Ulusal Hakemli Dergi - Kurum Öğretim Elemanı | en_US] |
