ORCID Profile
0000-0002-8893-2181
Current Organisation
Deakin Univeristy
Does something not look right? The information on this page has been harvested from data sources that may not be up to date. We continue to work with information providers to improve coverage and quality. To report an issue, use the Feedback Form.
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2018
Publisher: IEEE
Date: 07-2010
Publisher: IEEE
Date: 10-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2023
Publisher: Elsevier BV
Date: 04-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2010
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 10-2012
Publisher: IEEE
Date: 10-2012
Publisher: Institution of Engineering and Technology (IET)
Date: 2014
Publisher: Institution of Engineering and Technology (IET)
Date: 11-2015
Publisher: IEEE
Date: 11-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2022
Publisher: Elsevier BV
Date: 12-2022
Publisher: IEEE
Date: 11-2017
Publisher: Science Alert
Date: 15-06-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 11-2003
Publisher: IEEE
Date: 10-2017
Publisher: Elsevier BV
Date: 2017
Publisher: IEEE
Date: 09-2015
Publisher: IEEE
Date: 02-2013
Publisher: IEEE
Date: 10-2012
Publisher: IEEE
Date: 12-2007
Publisher: IEEE
Date: 10-2016
Publisher: IEEE
Date: 12-2017
Publisher: IEEE
Date: 08-2018
Publisher: ACM
Date: 19-03-2018
Publisher: Int. Acad. Publishers
Date: 2000
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 03-2003
Publisher: IEEE
Date: 07-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: IEEE
Date: 10-2017
Publisher: IEEE
Date: 07-2017
Publisher: IEEE
Date: 02-2010
Publisher: Elsevier BV
Date: 07-2018
Publisher: IEEE
Date: 12-2017
Publisher: IEEE
Date: 09-2014
Publisher: IEEE
Date: 07-2015
Publisher: IEEE
Date: 2010
Publisher: IEEE
Date: 11-2018
Publisher: Informa UK Limited
Date: 14-12-2017
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 02-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2020
Publisher: IEEE
Date: 09-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2014
Publisher: IEEE
Date: 07-2016
Publisher: Elsevier BV
Date: 12-2015
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 10-2016
Publisher: IEEE
Date: 09-2018
Publisher: IEEE
Date: 07-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Hindawi Limited
Date: 29-04-2014
DOI: 10.1002/ETEP.1933
Publisher: IEEE
Date: 07-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 10-2018
Publisher: IEEE
Date: 07-2016
Publisher: IEEE
Date: 11-2010
Publisher: IEEE
Date: 10-2013
Publisher: IEEE
Date: 06-2017
Publisher: Springer Science and Business Media LLC
Date: 19-11-2015
Publisher: Informa UK Limited
Date: 2014
Publisher: IEEE
Date: 10-2014
Publisher: IEEE
Date: 07-2011
Publisher: IEEE
Date: 08-2018
Publisher: IEEE
Date: 09-2008
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 06-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2021
Publisher: IEEE
Date: 05-2007
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 12-2022
Publisher: World Scientific Pub Co Pte Lt
Date: 12-2017
DOI: 10.1142/S1469026817500262
Abstract: Accurate prediction of wind power is of vital importance for demand management. In this paper, we adopt a cluster-based ensemble framework to predict wind power. Natural groups/clusters exist in datasets and learning algorithms benefit from group/cluster wise learning — a philosophy that is not well explored for wind power prediction. The research presented in this paper investigates this philosophy to predict wind power by using an ensemble of regression models on natural clusters within wind data. We have conducted a series of experiments on a large number of locations across Australia and analyzed the existence of clusters within wind data, suitability of linear and nonlinear regression models for the proposed framework, and how well the cluster-based ensemble performs against the situation when no clustering is done. Experimental results demonstrate prediction improvement as high as 17.94% through the usage of the cluster-based ensemble regression algorithm.
Publisher: IEEE
Date: 07-2012
Publisher: IEEE
Date: 07-2011
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 2022
Publisher: Informa UK Limited
Date: 02-10-2017
Publisher: IEEE
Date: 07-2015
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2021
Publisher: IEEE
Date: 08-2018
Publisher: Institution of Engineering and Technology (IET)
Date: 2009
Publisher: IEEE
Date: 2013
Publisher: Springer Science and Business Media LLC
Date: 20-10-2018
Publisher: IEEE
Date: 07-2008
Publisher: IEEE
Date: 10-2016
Publisher: IEEE
Date: 2001
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: IEEE
Date: 12-2012
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 05-2018
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 07-2019
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 09-2019
Publisher: Informa UK Limited
Date: 2011
Publisher: IEEE
Date: 11-2008
Publisher: IEEE
Date: 06-2016
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 08-2004
Publisher: IEEE
Date: 02-2013
Publisher: IEEE
Date: 02-2013
Publisher: Institute of Electrical and Electronics Engineers (IEEE)
Date: 04-2013
Publisher: IEEE
Date: 10-2012
No related grants have been discovered for Md Enamul Haque.