Study on the estimation of wind energy generation using artificial neural networks

Saraçoğlu R., Altın M. C.

Fresenius Environmental Bulletin, vol.29, no.9, pp.7826-7831, 2020 (SCI-Expanded) identifier identifier

  • Publication Type: Article / Article
  • Volume: 29 Issue: 9
  • Publication Date: 2020
  • Journal Name: Fresenius Environmental Bulletin
  • Journal Indexes: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Aerospace Database, Agricultural & Environmental Science Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Chemical Abstracts Core, Communication Abstracts, Environment Index, Geobase, Greenfile, Metadex, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Page Numbers: pp.7826-7831
  • Van Yüzüncü Yıl University Affiliated: Yes


© 2020 Parlar Scientific Publications. All rights reserved.Reducing environmental pollution and protecting the environment is the most urgent need of our world. Since non renewable resources used to meet the energy demand in the world produce large amounts of greenhouse gases, environmental problems occures. The supply of non renewable resources will be more expensive as their reserves will decrease and become depleted in the coming years. Renewable energy sources are clean, environmentally friendly and do not require any raw materials for production. Wind power generation systems, which stand out in the production of renewable energy sources, gain importance considering the current wind energy potential. Although electricity production from wind energy has increased, it is still not considered a safe energy source for the electricity grid. Since wind is a variable (unbalanced and unbalanced) source, it is difficult to predict. Wind production estimates are needed to ensure that the energy produced does not have grid adaptation problems and to make effective use of the energy produced. In this study, a model was created to estimate wind speed using artificial neural networks based methods and meteorological data. Wind energy potentials can be calculated regionally with these and similar models. Energy production that does not harm the environment can be realized better.