Hybrid optimisation and machine learning models for wind and solar data prediction

Amoura, Yahia; Torres, Santiago; Lima, José; Pereira, Ana I.

Publication

Hybrid optimisation and machine learning models for wind and solar data prediction

2023Journal article

dc.contributor.author	Amoura, Yahia
dc.contributor.author	Torres, Santiago
dc.contributor.author	Lima, José
dc.contributor.author	Pereira, Ana I.
dc.date.accessioned	2024-06-17T13:46:27Z
dc.date.available	2024-06-17T13:46:27Z
dc.date.issued	2023
dc.description.abstract	The exponential growth in energy demand is leading to massive energy consumption from fossil resources causing a negative effects for the environment. It is essential to promote sustainable solutions based on renewable energies infrastructures such as microgrids integrated to the existing network or as stand alone solution. Moreover, the major focus of today is being able to integrate a higher percentages of renewable electricity into the energy mix. The variability of wind and solar energy requires knowing the relevant long-term patterns for developing better procedures and capabilities to facilitate integration to the network. Precise prediction is essential for an adequate use of these renewable sources. This article proposes machine learning approaches compared to an hybrid method, based on the combination of machine learning with optimisation approaches. The results show the improvement in the accuracy of the machine learning models results once the optimisation approach is used.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Amoura, Yahia; Torres, Santiago; Lima, José; Pereira, Ana I. (2023). Hybrid optimisation and machine learning models for wind and solar data prediction. International Journal of Hybrid Intelligent Systems. ISSN 1448-5869. 19:1-2, p. 45-60	pt_PT
dc.identifier.doi	10.3233/HIS-230004	pt_PT
dc.identifier.issn	1448-5869
dc.identifier.uri	http://hdl.handle.net/10198/29905
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.publisher	IOS Press	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Forecasting	pt_PT
dc.subject	Machine learning	pt_PT
dc.subject	Optimisation	pt_PT
dc.subject	Renewable energy	pt_PT
dc.subject	Solar irradiation	pt_PT
dc.subject	Wind speed	pt_PT
dc.title	Hybrid optimisation and machine learning models for wind and solar data prediction	pt_PT
dc.type	journal article
dspace.entity.type	Publication
oaire.citation.endPage	60	pt_PT
oaire.citation.issue	1-2	pt_PT
oaire.citation.startPage	45	pt_PT
oaire.citation.title	International Journal of Hybrid Intelligent Systems	pt_PT
oaire.citation.volume	19	pt_PT
person.familyName	Amoura
person.familyName	Lima
person.familyName	Pereira
person.givenName	Yahia
person.givenName	José
person.givenName	Ana I.
person.identifier	R-000-8GD
person.identifier.ciencia-id	1C1C-915D-DB4E
person.identifier.ciencia-id	6016-C902-86A9
person.identifier.ciencia-id	0716-B7C2-93E4
person.identifier.orcid	0000-0002-8811-0823
person.identifier.orcid	0000-0001-7902-1207
person.identifier.orcid	0000-0003-3803-2043
person.identifier.rid	L-3370-2014
person.identifier.rid	F-3168-2010
person.identifier.scopus-author-id	55851941311
person.identifier.scopus-author-id	15071961600
rcaap.rights	openAccess	pt_PT
rcaap.type	article	pt_PT
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