Forecasting the energy demand of Turkey with a NN based on an improved Particle Swarm Optimization-Reference-Cited by-同舟云学术

Forecasting the energy demand of Turkey with a NN based on an improved Particle Swarm Optimization

Published:2016-06-02 Issue:S1 Volume:28 Page:539-549
ISSN:0941-0643
Container-title:Neural Computing and Applications
language:en
Short-container-title:Neural Comput & Applic

Author:

Daş Gülesin Sena

Publisher

Springer Science and Business Media LLC

Subject

Artificial Intelligence,Software

Link

http://link.springer.com/article/10.1007/s00521-016-2367-8/fulltext.html

Reference62 articles.

1. International Energy Agency (2013). http://www.iea.org/newsroomandevents/pressreleases/2010/july/name,20287,en.html . Accessed 23 July 2013

2. Republic of Turkey Prime Ministry (2010) Investment Support and Development Agency of Turkey & Deloitte, Turkish Energy Industry Report

3. European Commission (2013) Eurostat. http://epp.eurostat.ec.europa.eu/statistics_explained/index.php/The_EU_in_the_world_-_energy#Energy_imports . Accessed 24 April 2013

4. Suganthi L, Samuel Anand A (2012) Energy models for demand forecasting—a review. Renew Sustain Energy Rev 16(2):1223–1240

5. Farahbakhsh H, Ugursal VI, Fung AS (1998) A residential end-use energy consumption model for Canada. Int J Energy Res 22(13):1133–1143

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