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Short-term wind speed forecasting by an adaptive network-based fuzzy inference system (ANFIS): An attempt towards an ensemble forecasting method

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dc.citedby9
dc.contributor.authorYousefi M.en_US
dc.contributor.authorHooshyar D.en_US
dc.contributor.authorDooraki A.R.en_US
dc.contributor.authorSahari K.S.M.en_US
dc.contributor.authorKhaksar W.en_US
dc.contributor.authorAlnaimi F.B.I.en_US
dc.contributor.authorid53985756300en_US
dc.contributor.authorid56572940600en_US
dc.contributor.authorid57189250021en_US
dc.contributor.authorid57218170038en_US
dc.contributor.authorid54960984900en_US
dc.contributor.authorid58027086700en_US
dc.date.accessioned2023-05-29T05:59:40Z
dc.date.available2023-05-29T05:59:40Z
dc.date.issued2015
dc.description.abstractAccurate Wind speed forecasting has a vital role in efficient utilization of wind farms. Wind forecasting could be performed for long or short time horizons. Given the volatile nature of wind and its dependent on many geographical parameters, it is difficult for traditional methods to provide a reliable forecast of wind speed time series. In this study, an attempt is made to establish an efficient adaptive network-based fuzzy interference (ANFIS) for short-term wind speed forecasting. Using the available data sets in the literature, the ANFIS network is constructed, tested and the results are compared with that of a regular neural network, which has been forecasted the same set of dataset in previous studies. To avoid trial-and-error process for selection of the ANFIS input data, the results of autocorrelation factor (ACF) and partial auto correlation factor (PACF) on the historical wind speed data are employed. The available data set is divided into two parts. 50% for training and 50% for testing and validation. The testing part of data set will be merely used for assessing the performance of the neural network which guarantees that only unseen data is used to evaluate the forecasting performance of the network. On the other hand, validation data could be used for parameter-setting of the network if required. The results indicate that ANFIS could not outperform ANN in short-term wind speed forecasting though its results are competitive. The two methods are hybridized, though simply by weightage, and the hybrid methods shows slight improvement comparing to both ANN and ANFIS results. Therefore, the goal of future studies could be implementing ANFIS and ANNs in a more comprehensive ensemble method which could be ultimately more robust and accurate. � 2015 International Journal of Advances in Intelligent Informatics. All rights reserved.en_US
dc.description.natureFinalen_US
dc.identifier.doi10.26555/ijain.v1i3.45
dc.identifier.epage149
dc.identifier.issue3
dc.identifier.scopus2-s2.0-85016192733
dc.identifier.spage140
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-85016192733&doi=10.26555%2fijain.v1i3.45&partnerID=40&md5=3d6182412dde7f524d79344d629b5451
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22219
dc.identifier.volume1
dc.publisherUniversitas Ahmad Dahlanen_US
dc.relation.ispartofAll Open Access, Gold, Green
dc.sourceScopus
dc.sourcetitleInternational Journal of Advances in Intelligent Informatics
dc.titleShort-term wind speed forecasting by an adaptive network-based fuzzy inference system (ANFIS): An attempt towards an ensemble forecasting methoden_US
dc.typeArticleen_US
dspace.entity.typePublication
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