A univariate time series methodology based on sequence-to-sequence learning for short to midterm wind power production

Akbal Y., Ünlü K. D.

Renewable Energy, cilt.200, ss.832-844, 2022 (SCI-Expanded) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 200
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1016/j.renene.2022.10.055
  • Dergi Adı: Renewable Energy
  • Derginin Tarandığı İndeksler: Science Citation Index Expanded (SCI-EXPANDED), Scopus, Academic Search Premier, PASCAL, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), CAB Abstracts, Communication Abstracts, Compendex, Environment Index, Geobase, Greenfile, Index Islamicus, INSPEC, Pollution Abstracts, Public Affairs Index, Veterinary Science Database, DIALNET, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.832-844
  • Anahtar Kelimeler: LSTM, GRU, Turkey, Wind power, Electricity production, Time series analysis
  • TED Üniversitesi Adresli: Evet

Özet

© 2022 Elsevier LtdThe biggest wind farm of Turkey is placed at Manisa which is located in the Aegean Region. Electricity is a non-storable commodity for that reason, it is very important to have a strong forecast and model of the potential electricity production to plan the electricity loads. In this study, the aim is to model and forecast electricity production of the wind farms located at Manisa by using a univariate model based on sequence-to-sequence learning. The forecasting range of the study is from short term to midterm. The strength of the proposed model is that; it only needs its own lagged value to make forecasts. The empirical evidences show that the model has high coefficient of variation (R2) in short term and moderate R2 in the midterm forecast. Although in the mid-range forecasts R2 slightly decreases mean squared error and mean absolute error shows that the model is accurate also in the midterm forecasts. The proposed model is not only strong in hourly electricity production forecasts but with a slight modification also in forecasting the minimum, maximum and average electricity production for a fixed range. This study concludes with two fresh and intriguing future research ideas.