Combined optimization and regression machine learning for solar Irradiation and wind speed forecasting

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

Publication

Combined optimization and regression machine learning for solar Irradiation and wind speed forecasting

2023Conference object

dc.contributor.author	Amoura, Yahia
dc.contributor.author	Torres, Santiago
dc.contributor.author	Lima, José
dc.contributor.author	Pereira, Ana I.
dc.date.accessioned	2023-02-28T10:41:17Z
dc.date.available	2023-02-28T10:41:17Z
dc.date.issued	2023
dc.description.abstract	Prediction of solar irradiation and wind speed are essential for enhancing the renewable energy integration into the existing power system grids. However, the deficiencies caused to the network operations provided by their intermittent effects need to be investigated. Regarding reserves management, regulation, scheduling, and dispatching, the intermittency in power output become a challenge for the system operator. This had given the interest of researchers for developing techniques to predict wind speeds and solar irradiation over a large or short-range of temporal and spatial perspectives to accurately deal with the variable power output. Before, several statistical, and even physics, approaches have been applied for prediction. Nowadays, machine learning is widely applied to do it and especially regression models to assess them. Tuning these models is usually done following manual approaches by changing the minimum leaf size of a decision tree, or the box constraint of a support vector machine, for example, that can affect its performance. Instead of performing it manually, this paper proposes to combine optimization methods including the bayesian optimization, grid search, and random search with regression models to extract the best hyper parameters of the model. Finally, the results are compared with the manually tuned models. The Bayesian gives the best results in terms of extracting hyper-parameters by giving more accurate models.	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). Combined optimization and regression machine learning for solar Irradiation and wind speed forecasting. In 2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2. Bragança	pt_PT
dc.identifier.uri	http://hdl.handle.net/10198/27278
dc.language.iso	eng	pt_PT
dc.peerreviewed	yes	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Renewable energy	pt_PT
dc.subject	Forecasting	pt_PT
dc.subject	Machine learning	pt_PT
dc.subject	Optimization	pt_PT
dc.subject	Wind speed	pt_PT
dc.subject	Solar irradiation	pt_PT
dc.title	Combined optimization and regression machine learning for solar Irradiation and wind speed forecasting	pt_PT
dc.type	conference object
dspace.entity.type	Publication
oaire.citation.conferencePlace	Bragança	pt_PT
oaire.citation.title	2nd International Conference on Optimization, Learning Algorithms and Applications, OL2A 2022	pt_PT
person.familyName	YAHIA AMOURA
person.familyName	Lima
person.familyName	Pereira
person.givenName	YAHIA AMOURA
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	conferenceObject	pt_PT
relation.isAuthorOfPublication	653c4356-dd18-4680-9774-da86a446d0e5
relation.isAuthorOfPublication	d88c2b2a-efc2-48ef-b1fd-1145475e0055
relation.isAuthorOfPublication	e9981d62-2a2b-4fef-b75e-c2a14b0e7846
relation.isAuthorOfPublication.latestForDiscovery	653c4356-dd18-4680-9774-da86a446d0e5