Multi-time series and-time scale modeling for wind speed and wind power forecasting part II: Medium-term and long-term applications

Colak I., SAĞIROĞLU Ş., Yesilbudak M., Kabalci E., Ibrahim Bulbul H.

4th International Conference on Renewable Energy Research and Applications, ICRERA 2015, Palermo, Italy, 22 - 25 November 2015, pp.215-220, (Full Text) identifier

  • Publication Type: Conference Paper / Full Text
  • Doi Number: 10.1109/icrera.2015.7418698
  • City: Palermo
  • Country: Italy
  • Page Numbers: pp.215-220
  • Keywords: forecating, long-term, medium-term, Time series methods, wind power, wind speed
  • Istanbul Gelisim University Affiliated: No

Abstract

© 2015 IEEE.This paper represents the second part of an entire study which focuses on multi-time series and-time scale modeling in wind speed and wind power forecasting. In the first part of the entire study [1], firstly, moving average (MA), weighted moving average (WMA), autoregressive moving average (ARMA) and autoregressive integrated moving average (ARIMA) models are introduced in-depth. Afterwards, the mentioned models are analyzed for very short-term and short-term forecasting scales, comprehensively. In this second part of the entire study, we address the medium-term and long-term prediction performance of MA, WMA, ARMA and ARIMA models in wind speed and wind power forecasting. Particularly, 3-h and 6-h time series forecasting models are constructed in order to carry out 9-h and 24-h ahead forecasting, respectively. Many valuable assessments are made for the employed statistical models in terms of medium-term and long-terms forecasting scales. Finally, many valuable achievements are discussed considering a detailed comparison chart of the entire study.