A Synthetic Forecast Engine for Wind Power Prediction

Venera Nurmanova, Mehdi Bagheri, Oveis Abedinia, Behrouz Sobhani, Noradin Ghadimi, S. Moahammad

Research output: Chapter in Book/Report/Conference proceedingConference contribution

2 Citations (Scopus)

Abstract

Due to rapid growth of the wind power generation, this green energy becomes crucial in all over the globe. However, high volatility and non-convex behavior of this energy makes different problems in power system planning and operation. Hence, an accurate prediction method is required to addressing this specified issue. This study, provides a new forecasting approach based on new hybrid wavelet transform, feature selection as well as synthetic forecasting engine. The proposed engine includes three parallel blocks of NN (denoting the neural network), radial basis function NN as well as the SVM (support vector machine). The optimal values for all the forecasting engine variables are obtained using a meta-heuristic optimization method. Effectiveness of recommended prediction approach is applied on New England wind farm test case and compared with other strategies. Generated numerical results proof the validity of suggested approach.

Original languageEnglish
Title of host publication7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018
PublisherInstitute of Electrical and Electronics Engineers Inc.
Pages732-737
Number of pages6
ISBN (Electronic)9781538659823
DOIs
Publication statusPublished - Dec 6 2018
Event7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018 - Paris, France
Duration: Oct 14 2018Oct 17 2018

Publication series

Name7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018

Conference

Conference7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018
CountryFrance
CityParis
Period10/14/1810/17/18

Keywords

  • Feature selection
  • Synthetic forecast engine
  • Wavelet Transform
  • Wind power

ASJC Scopus subject areas

  • Energy Engineering and Power Technology
  • Renewable Energy, Sustainability and the Environment
  • Electrical and Electronic Engineering

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  • Cite this

    Nurmanova, V., Bagheri, M., Abedinia, O., Sobhani, B., Ghadimi, N., & Moahammad, S. (2018). A Synthetic Forecast Engine for Wind Power Prediction. In 7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018 (pp. 732-737). [8567010] (7th International IEEE Conference on Renewable Energy Research and Applications, ICRERA 2018). Institute of Electrical and Electronics Engineers Inc.. https://doi.org/10.1109/ICRERA.2018.8567010