Publication:
An artificial neural network hybrid with wavelet transform for short-term wind speed forecasting: A preliminary case study

Simple item page
dc.citedby14
dc.contributor.authorYousefi M.en_US
dc.contributor.authorHooshyar D.en_US
dc.contributor.authorYousefi M.en_US
dc.contributor.authorKhaksar W.en_US
dc.contributor.authorSahari K.S.M.en_US
dc.contributor.authorAlnaimi F.B.I.en_US
dc.contributor.authorid53985756300en_US
dc.contributor.authorid56572940600en_US
dc.contributor.authorid55247052200en_US
dc.contributor.authorid54960984900en_US
dc.contributor.authorid57218170038en_US
dc.contributor.authorid58027086700en_US
dc.date.accessioned2023-05-29T06:12:39Z
dc.date.available2023-05-29T06:12:39Z
dc.date.issued2016
dc.descriptionAlgorithms; Big data; Feedforward neural networks; Forecasting; Neural networks; Signal processing; Speed; Statistical tests; Time series; Wind; Wind power; Auto-correlation factors; Forecasting accuracy; Forecasting performance; Inverse wavelet transforms; Neural network predictions; Short-term wind speed forecasting; Trial-and-error process; Wind speed forecasting; Wavelet transformsen_US
dc.description.abstractGiven the importance of an accurate wind speed forecasting for efficient utilization of wind farms, and the volatile nature of wind speed data including its non-linear and uncertain nature, the wind speed forecasting has remained an active field of research. In this study, the non-linearity of wind speed is tackled using artificial neural network and its uncertainty by wavelet transform. To avoid trial-and-error process for selection the ANN structure, the results of auto correlation factor (ACF) and partial auto correlation factor (PACF) on the historical wind speed data are employed. Instead of forecasting the time series directly, a set of better-behaved components of the data is achieved by decomposing the data using wavelet transform and are forecasted separately using a feedforward neural network. Finally, using an inverse wavelet transform, the future time series is reconstructed and the wind speed could be forecasted. The historical hourly wind speed from ABEI weather station in Idaho, United States is used for assessing the performance of the proposed algorithm. This data set is merely selected due to its availability. The data is divided to three parts of 50%, 25% and 25% for training, testing and validation respectively. 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 shows that using wavelet transform can enhance the forecasting accuracy when it is compared with a regular neural network prediction algorithm. � 2015 IEEE.en_US
dc.description.natureFinalen_US
dc.identifier.ArtNo7407784
dc.identifier.doi10.1109/ICSITech.2015.7407784
dc.identifier.epage99
dc.identifier.scopus2-s2.0-84966508437
dc.identifier.spage95
dc.identifier.urihttps://www.scopus.com/inward/record.uri?eid=2-s2.0-84966508437&doi=10.1109%2fICSITech.2015.7407784&partnerID=40&md5=61c4c75cc73eba3dc22a54b54f0f3c5a
dc.identifier.urihttps://irepository.uniten.edu.my/handle/123456789/22844
dc.publisherInstitute of Electrical and Electronics Engineers Inc.en_US
dc.sourceScopus
dc.sourcetitleProceedings - 2015 International Conference on Science in Information Technology: Big Data Spectrum for Future Information Economy, ICSITech 2015
dc.titleAn artificial neural network hybrid with wavelet transform for short-term wind speed forecasting: A preliminary case studyen_US
dc.typeConference Paperen_US
dspace.entity.typePublication
Files
Collections
SCOPUS