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Wind speed forecast model for wind farm based on a hybrid machine learning algorithm

journal contribution
posted on 2023-05-17, 21:36 authored by Haque, AU, Mandal, P, Meng, J, Michael NegnevitskyMichael Negnevitsky
This paper presents a newstrategy for wind speed forecasting based on a hybrid machine learning algorithm, composed of a data filtering technique based on wavelet transform (WT) and a soft computing model based on the fuzzy ARTMAP (FA) network. The prediction capability of the proposed hybrid WT + FA model is demonstrated by an extensive comparison with some other existing wind speed forecasting methods. The results show a significant improvement in forecasting error through the application of a proposed hybrid WT + FA model. The proposed wind speed forecasting strategy is applied to real data acquired from the North Cape wind farm located in PEI, Canada.

History

Publication title

International Journal of Sustainable Energy

Volume

34

Pagination

35-51

ISSN

1478-6451

Department/School

School of Engineering

Publisher

Taylor & Francis

Place of publication

Abingdon, Oxfordshire UK

Rights statement

Copyright 2013 Taylor and Francis

Repository Status

  • Restricted

Socio-economic Objectives

Renewable energy not elsewhere classified