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Wind speed forecast model for wind farm based on a hybrid machine learning algorithm
Citation
Haque, AU and Mandal, P and Meng, J and Negnevitsky, M, Wind speed forecast model for wind farm based on a hybrid machine learning algorithm, International Journal of Sustainable Energy, 34, (1) pp. 35-51. ISSN 1478-6451 (2015) [Refereed Article]
Copyright Statement
Copyright 2013 Taylor and Francis
DOI: doi:10.1080/14786451.2013.826224
Abstract
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.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | fuzzy ARTMAP network, machine learning algorithm, short-term wind speed forecasting, wavelet transform |
| Research Division: | Engineering |
| Research Group: | Electrical engineering |
| Research Field: | Electrical energy generation (incl. renewables, excl. photovoltaics) |
| Objective Division: | Energy |
| Objective Group: | Renewable energy |
| Objective Field: | Renewable energy not elsewhere classified |
| UTAS Author: | Negnevitsky, M (Professor Michael Negnevitsky) |
| ID Code: | 88395 |
| Year Published: | 2015 (online first 2013) |
| Deposited By: | Engineering |
| Deposited On: | 2014-01-31 |
| Last Modified: | 2017-11-06 |
| Downloads: | 1 View Download Statistics |
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