A green energy research: forecasting of wind power for a cleaner environment using robust hybrid metaheuristic model

Creative Commons License

Kerem A., SAYGIN A., Rahmani R.

Environmental Science and Pollution Research, cilt.29, sa.34, ss.50998-51010, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 29 Sayı: 34
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1007/s11356-021-16494-7
  • Dergi Adı: Environmental Science and Pollution Research
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, IBZ Online, ABI/INFORM, Aerospace Database, Aqualine, Aquatic Science & Fisheries Abstracts (ASFA), BIOSIS, CAB Abstracts, EMBASE, Environment Index, Geobase, MEDLINE, Pollution Abstracts, Veterinary Science Database, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.50998-51010
  • Anahtar Kelimeler: Artificial neural network (ANN), Green energy, Hybrid metaheuristic model, Particle swarm optimization (PSO), Radial movement optimization (RMO), Renewable energy, Sustainability, Wind power forecasting
  • Gazi Üniversitesi Adresli: Evet

Özet

© 2021, The Author(s), under exclusive licence to Springer-Verlag GmbH Germany, part of Springer Nature.Wind is a stochastic and intermittent renewable energy source. Due to its nature, it is extremely hard to forecast wind power. Accurate wind power forecasting can be encouraging and motivating for investors to shed light on future uncertainties caused by global warming. Thus, CO2 and other greenhouse gases (GHG) which are harmful to the environment will not be released into the atmosphere, while generating electrical energy. This paper presents a novel precise, fast and powerful hybrid metaheuristic wind power forecasting approach based on statistical and mathematical data from real weather stations. The model was developed as a hybrid metaheuristic algorithm based on artificial neural networks (ANNs), particle swarm optimization (PSO) and radial movement optimization (RMO). Real-time wind data was gathered from wind measuring stations (WMS) at two separate places in Burdur and Osmaniye cities, Turkey. The key contribution of this new model is the ability to perform wind power forecasting studies, without needing wind speed data, with high accuracy and rapid solutions. Also, wind power forecasting studies with high accuracy have been carried out despite the height differences between the sensors. That is, for WMS-1 and WMS-2, it has succeeded the wind power forecasting at 61 m and 60.3 m using temperature (3 m), humidity (3 m) and pressure (3.5 m) data. The performance results were presented in tables and graphs.