A short term wind speed forecasting model using artificial neural network and adaptive neuro-fuzzy inference system models

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

Publication

A short term wind speed forecasting model using artificial neural network and adaptive neuro-fuzzy inference system models

2022Conference paper

dc.contributor.author	Amoura, Yahia
dc.contributor.author	Pereira, Ana I.
dc.contributor.author	Lima, José
dc.date.accessioned	2023-03-16T11:30:43Z
dc.date.available	2023-03-16T11:30:43Z
dc.date.issued	2022
dc.description.abstract	Future power systems encourage the use of renewable energy resources, among them wind power is of great interest, but its power output is intermittent in nature which can affect the stability of the power system and increase the risk of blackouts. Therefore, a forecasting model of the wind speed is essential for the optimal operation of a power supply with an important share of wind energy conversion systems. In this paper, two wind speed forecasting models based on multiple meteorological measurements of wind speed and temperature are proposed and compared according to their mean squared error (MSE) value. The first model concerns the artificial intelligence based on neural network (ANN) where several network configurations are proposed to achieve the most suitable structure of the problem, while the other model concerned the Adaptive Neuro-Fuzzy Inference System (ANFIS). To enhance the results accuracy, the invalid input samples are filtered. According to the computational results of the two models, the ANFIS has delivered more accurate outputs characterized by a reduced mean squared error value compared to the ANN-based model.	pt_PT
dc.description.version	info:eu-repo/semantics/publishedVersion	pt_PT
dc.identifier.citation	Amoura, Yahia; Pereira, Ana I.; Lima, José (2022). A short term wind speed forecasting model using artificial neural network and adaptive neuro-fuzzy inference system models. In 3rd EAI International Conference on Sustainable Energy for Smart Cities (SESC). 425, p. 189-204	pt_PT
dc.identifier.doi	10.1007/978-3-030-97027-7_12	pt_PT
dc.identifier.issn	18678211
dc.identifier.uri	http://hdl.handle.net/10198/27778
dc.language.iso	eng	pt_PT
dc.rights.uri	http://creativecommons.org/licenses/by/4.0/	pt_PT
dc.subject	Adaptive neuro-fuzzy inference system	pt_PT
dc.subject	Artificial neural network	pt_PT
dc.subject	Mean square error	pt_PT
dc.subject	Temperature	pt_PT
dc.subject	Wind speed	pt_PT
dc.title	A short term wind speed forecasting model using artificial neural network and adaptive neuro-fuzzy inference system models	pt_PT
dc.type	conference paper
dspace.entity.type	Publication
oaire.citation.endPage	204	pt_PT
oaire.citation.startPage	189	pt_PT
oaire.citation.title	3rd EAI International Conference on Sustainable Energy for Smart Cities (SESC)	pt_PT
oaire.citation.volume	425	pt_PT
person.familyName	Amoura
person.familyName	Pereira
person.familyName	Lima
person.givenName	Yahia
person.givenName	Ana I.
person.givenName	José
person.identifier	R-000-8GD
person.identifier.ciencia-id	1C1C-915D-DB4E
person.identifier.ciencia-id	0716-B7C2-93E4
person.identifier.ciencia-id	6016-C902-86A9
person.identifier.orcid	0000-0002-8811-0823
person.identifier.orcid	0000-0003-3803-2043
person.identifier.orcid	0000-0001-7902-1207
person.identifier.rid	F-3168-2010
person.identifier.rid	L-3370-2014
person.identifier.scopus-author-id	15071961600
person.identifier.scopus-author-id	55851941311
rcaap.rights	restrictedAccess	pt_PT
rcaap.type	conferenceObject	pt_PT
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