A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data

Sharifian, A; Ghadi, MJ; Ghavidel, S; Li, L; Zhang, J

A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data

Sharifian, A Ghadi, MJ

Ghavidel, S Li, L

Zhang, J

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Publication Type:: Journal Article
Citation:: Renewable Energy, 2018, 120 pp. 220 - 230
Issue Date:: 2018-05-01

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Field	Value	Language
dc.contributor.author	Sharifian, A	en_US
dc.contributor.author	Ghadi, MJ https://orcid.org/0000-0002-5564-5055	en_US
dc.contributor.author	Ghavidel, S	en_US
dc.contributor.author	Li, L https://orcid.org/0000-0001-8485-2216	en_US
dc.contributor.author	Zhang, J https://orcid.org/0000-0003-2616-6722	en_US
dc.date.available	2020-06-01T19:02:07Z
dc.date.issued	2018-05-01	en_US
dc.identifier.citation	Renewable Energy, 2018, 120 pp. 220 - 230	en_US
dc.identifier.issn	0960-1481	en_US
dc.identifier.uri	http://hdl.handle.net/10453/125509
dc.description.abstract	© 2017 Elsevier Ltd Nowadays, due to some environmental restrictions and decrease of fossil fuel sources, renewable energy sources and specifically wind power plants have a major part of energy generation in the industrial countries. To this end, the accurate forecasting of wind power is considered as an important and influential factor for the management and planning of power systems. In this paper, a novel intelligent method is proposed to provide an accurate forecast of the medium-term and long-term wind power by using the uncertain data from an online supervisory control and data acquisition (SCADA) system and the numerical weather prediction (NWP). This new method is based on the particle swarm optimization (PSO) algorithm and applied to train the Type-2 fuzzy neural network (T2FNN) which is called T2FNN-PSO. The presented method combines both of fuzzy system's expert knowledge and the neural network's learning capability for accurate forecasting of the wind power. In addition, the T2FNN-PSO can appropriately handle the uncertainties associated with the measured parameters from SCADA system, the numerical weather prediction and measuring tools. The proposed method is applied on a case study of a real wind farm. The obtained simulation results validate effectiveness and applicability of the proposed method for a practical solution to an accurate wind power forecasting in a power system control center.	en_US
dc.relation.ispartof	Renewable Energy	en_US
dc.relation.isbasedon	10.1016/j.renene.2017.12.023	en_US
dc.rights	info:eu-repo/semantics/openAccess
dc.subject.classification	Energy	en_US
dc.title	A new method based on Type-2 fuzzy neural network for accurate wind power forecasting under uncertain data	en_US
dc.type	Journal Article
utslib.citation.volume	120	en_US
utslib.for	0906 Electrical and Electronic Engineering	en_US
utslib.for	0913 Mechanical Engineering	en_US
utslib.for	0915 Interdisciplinary Engineering	en_US
pubs.embargo.period	Not known	en_US
pubs.organisational-group	/University of Technology Sydney
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology
pubs.organisational-group	/University of Technology Sydney/Faculty of Engineering and Information Technology/School of Electrical and Data Engineering
pubs.organisational-group	/University of Technology Sydney/Students
utslib.copyright.status	open_access	*
pubs.publication-status	Published	en_US
pubs.volume	120	en_US

Abstract:

© 2017 Elsevier Ltd Nowadays, due to some environmental restrictions and decrease of fossil fuel sources, renewable energy sources and specifically wind power plants have a major part of energy generation in the industrial countries. To this end, the accurate forecasting of wind power is considered as an important and influential factor for the management and planning of power systems. In this paper, a novel intelligent method is proposed to provide an accurate forecast of the medium-term and long-term wind power by using the uncertain data from an online supervisory control and data acquisition (SCADA) system and the numerical weather prediction (NWP). This new method is based on the particle swarm optimization (PSO) algorithm and applied to train the Type-2 fuzzy neural network (T2FNN) which is called T2FNN-PSO. The presented method combines both of fuzzy system's expert knowledge and the neural network's learning capability for accurate forecasting of the wind power. In addition, the T2FNN-PSO can appropriately handle the uncertainties associated with the measured parameters from SCADA system, the numerical weather prediction and measuring tools. The proposed method is applied on a case study of a real wind farm. The obtained simulation results validate effectiveness and applicability of the proposed method for a practical solution to an accurate wind power forecasting in a power system control center.

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http://hdl.handle.net/10453/125509