A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance

Please use this identifier to cite or link to this item: https://doi.org/10.1016/j.energy.2015.01.066

DC Field	Value
dc.title	A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance
dc.contributor.author	Dong, Zibo
dc.contributor.author	Yang, Dazhi
dc.contributor.author	Reindl Thomas Guenter
dc.contributor.author	Walsh, Wilfred Michael
dc.date.accessioned	2016-09-20T05:45:09Z
dc.date.available	2016-09-20T05:45:09Z
dc.date.issued	2015
dc.identifier.citation	Dong, Zibo, Yang, Dazhi, Reindl Thomas Guenter, Walsh, Wilfred Michael (2015). A novel hybrid approach based on self-organizing maps, support vector regression and particle swarm optimization to forecast solar irradiance. Energy 82 : 570-577. ScholarBank@NUS Repository. https://doi.org/10.1016/j.energy.2015.01.066
dc.identifier.issn	03605442
dc.identifier.uri	http://scholarbank.nus.edu.sg/handle/10635/128038
dc.description.uri	http://libproxy1.nus.edu.sg/login?url=http://dx.doi.org/10.1016/j.energy.2015.01.066
dc.publisher	Elsevier
dc.type	Article
dc.contributor.department	SOLAR ENERGY RESEARCH INST OF S'PORE
dc.description.doi	10.1016/j.energy.2015.01.066
dc.description.sourcetitle	Energy
dc.description.volume	82
dc.description.page	570-577
dc.identifier.isiut	000351788700051
dc.published.state	Published
Appears in Collections:	Staff Publications

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