BIG DATA ANALYTICS FOR DETERMINING TURKEY'S WIND POWER STATUS AND POWER GENERATION POTENTIAL: SMART MANAGEMENT OF RENEWABLE AND SUSTAINABLE ENERGY RESOURCES

Kayadelen A. N., ANTMEN Z. F., EROL H.

FRESENIUS ENVIRONMENTAL BULLETIN, cilt.31, sa.9, ss.9294-9312, 2022 (SCI-Expanded) identifier

  • Yayın Türü: Makale / Derleme
  • Cilt numarası: 31 Sayı: 9
  • Basım Tarihi: 2022
  • Dergi Adı: FRESENIUS ENVIRONMENTAL BULLETIN
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Aerospace 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
  • Sayfa Sayıları: ss.9294-9312
  • Anahtar Kelimeler: Big Data Analytics, Renewable Energy Sources, Wind Energy Potential, Weibull Distribution, Mixture Weibull Distribution, Average Power Density, CANAKKALE, FUTURE
  • Çukurova Üniversitesi Adresli: Evet

Özet

This study is about big data analytics for determining Turkey's wind power status and power generation potential and Smart Management of Renewable and Sustainable Energy Resources, It aims to employ big data analytics to investigate Turkey's capacity to use its wind power potential for power generation in the country's seven geographical regions and all cities in the relevant regions. To this end, the monthly average wind speed, pressure and temperature data for the last 5 years (2012 - 2016) obtained from the General Directorate of State Meteorological Service were used. The appropriate statistical distributions of wind speeds as well as the parameters of such distribution were determined. The essential data required for determining the wind power potential in a region is the wind speed while the key quantity used to this end is the average power density. Therefore, this study calculates the actual values as well as the average power density values estimated by the Weibull Distribution and the Mixture Weibull Distribution functions. In conclusion, this study establishes a Big Data Analytics Method to determine Turkey's Wind Power Status and Wind Power Generation Potential for the Smart Management of Renewable and Sustainable Energy Resources.