Short-term wind speed forecast for Urla wind power plant: A hybrid approach that couples weather research and forecasting model, weather patterns and SCADA data with comprehensive data preprocessing

Ozen C., Deniz A.

WIND ENGINEERING, cilt.46, sa.5, ss.1526-1549, 2022 (ESCI) identifier identifier

  • Yayın Türü: Makale / Tam Makale
  • Cilt numarası: 46 Sayı: 5
  • Basım Tarihi: 2022
  • Doi Numarası: 10.1177/0309524x221088612
  • Dergi Adı: WIND ENGINEERING
  • Derginin Tarandığı İndeksler: Emerging Sources Citation Index (ESCI), Scopus, Aerospace Database, Aquatic Science & Fisheries Abstracts (ASFA), Communication Abstracts, Compendex, Environment Index, Geobase, INSPEC, Metadex, Civil Engineering Abstracts
  • Sayfa Sayıları: ss.1526-1549
  • Anahtar Kelimeler: Wind speed forecasting, WRF model, outlier detection, data treatment, missing data imputation, weather, NETWORK
  • İstanbul Teknik Üniversitesi Adresli: Evet

Özet

Short-term wind speed forecast model that uses both supervisory control and data acquisition (SCADA) based data and weather research and forecasting (WRF) model outputs for Urla wind power plant (WPP) has been proposed in this study. Two different WRF models were run to gather atmospheric variables from four surrounding grids of Urla WPP and calculate weather patterns affecting Urla WPP. After detecting outliers in the SCADA data by coupling of k-mean and isolation forest (IF) methods, statistical methods were used for data treatment and the outputs of WRF models were used for missing data imputation. The effect of each data type and data preprocessing techniques on the model was evaluated separately. The best model performance was achieved with 0.9085 R-2, and 0.81 MAE in the dataset which includes each data type and each data preprocessing was applied on. Otherwise, the dominant weather pattern affecting Urla WPP was found to be purely advective and the best result was achieved in this pattern.