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A new prediction model based on cascade NN for wind power prediction

Torabi, A ; Sharif University of Technology | 2018

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  1. Type of Document: Article
  2. DOI: 10.1007/s10614-018-9795-8
  3. Publisher: Springer New York LLC , 2018
  4. Abstract:
  5. This paper presents a new prediction model based on empirical mode decomposition, feature selection and hybrid forecast engine. The whole structure of proposed model is based on nonstationarity and non-convex nature of wind power signal. The hybrid forecast engine consists of three main stages as; empirical mode decomposition, an intelligent algorithm and three stage neural network. All parameters of proposed neural network will be optimized by intelligent algorithm. Effectiveness of the proposed model is tested with real-world hourly data of wind farms in Canada, Spain and Texas. In order to demonstrate the validity of the proposed model, it is compared with several other wind speed and power forecast techniques where, different prediction horizons are considered from day-ahead to one week forecasting. Obtained results confirm the validity of the developed approach in prediction model for different forecast horizons. © 2018 Springer Science+Business Media, LLC, part of Springer Nature
  6. Keywords:
  7. Cascade NN ; EEMD ; Feature selection ; Wind power forecast
  8. Source: Computational Economics ; March , 2018 , Pages 1-25 ; 09277099 (ISSN)
  9. URL: https://link.springer.com/article/10.1007%2Fs10614-018-9795-8