Hybrid model for short term wind speed forecasting using empirical mode decomposition and artificial neural network

DOKUR E., KURBAN M., CEYHAN S.

9th International Conference on Electrical and Electronics Engineering, ELECO 2015, Bursa, Türkiye, 26 - 28 Kasım 2015, ss.420-423 identifier

  • Yayın Türü: Bildiri / Tam Metin Bildiri
  • Doi Numarası: 10.1109/eleco.2015.7394591
  • Basıldığı Şehir: Bursa
  • Basıldığı Ülke: Türkiye
  • Sayfa Sayıları: ss.420-423
  • Bilecik Şeyh Edebali Üniversitesi Adresli: Evet

Özet

© 2015 Chamber of Electrical Engineers of Turkey.Wind speed modeling and prediction plays a critical role in wind related engineering studies. With the integration of wind energy into electricity grids, it is becoming increasingly important to obtain accurate wind speed forecasts. Accurate wind speed forecasts are necessary to schedule dispatchable generation and tariffs in the electricity market. In this paper a hybrid model named EMD-ANN for wind speed prediction is proposed based on the Empirical Mode Decomposition (EMD) and the Artificial Neural Networks (ANN) for renewable energy systems. All the models are analyzed with real data of wind speeds in Bilecik, Turkey using data measurement from the Turkish State Meteorological Service. Accuracy of the forecasting is evaluated in terms of MAE and MSE.