Please use this identifier to cite or link to this item:
https://hdl.handle.net/20.500.14279/14180
DC Field | Value | Language |
---|---|---|
dc.contributor.author | Panapakidis, Ioannis P. | - |
dc.contributor.author | Michailides, Constantine | - |
dc.contributor.author | Angelides, Demos C. | - |
dc.date.accessioned | 2019-06-30T06:55:11Z | - |
dc.date.available | 2019-06-30T06:55:11Z | - |
dc.date.issued | 2019-04 | - |
dc.identifier.citation | Electronics, 2019, vol. 8, no. 4 | en_US |
dc.identifier.issn | 20799292 | - |
dc.description | This article belongs to the Special Issue Deep Learning Applications with Practical Measured Results in Electronics Industries | en_US |
dc.description.abstract | Wind speed forecasting is an important element for the further development of offshore wind turbines. Due to its importance, many researchers have proposed different models for wind speed forecasting that differ in terms of the time-horizon of the forecast, types and number of inputs, complexity, structure, and others. Wind speed series present high nonlinearity and volatilities, and thus an effective model should successfully deal with those features. An approach to deal with the nonlinearities and volatilities is to utilize a time series processing technique such as the wavelet transform. In the present paper, an ensemble data-driven short-term wind speed forecasting model is developed, tested and applied. The term “ensemble” refers to the combination of two different predictors that run in parallel and the prediction is obtained by the predictor that leads to the lowest error. The proposed model utilizes the wavelet transform and is compared with other models that have been presented in the related literature and outperforms their accuracy. The proposed forecasting model can be used effectively for 1 min and 10 min ahead horizon wind speed predictions. | en_US |
dc.format | en_US | |
dc.language.iso | en | en_US |
dc.relation.ispartof | Electronics | en_US |
dc.rights | © This is an open access article distributed under the Creative Commons Attribution License which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited | en_US |
dc.subject | Computational intelligence | en_US |
dc.subject | Offshore wind | en_US |
dc.subject | Forecasting | en_US |
dc.subject | Machine learning | en_US |
dc.subject | Neural networks | en_US |
dc.subject | Neuro-fuzzy systems | en_US |
dc.title | A data-driven short-term forecasting model for offshore wind speed prediction based on computational intelligence | en_US |
dc.type | Article | en_US |
dc.collaboration | University of Thessaly | en_US |
dc.collaboration | Cyprus University of Technology | en_US |
dc.collaboration | Aristotle University of Thessaloniki | en_US |
dc.subject.category | Electrical Engineering - Electronic Engineering - Information Engineering | en_US |
dc.journals | Open Access | en_US |
dc.country | Greece | en_US |
dc.country | Cyprus | en_US |
dc.subject.field | Engineering and Technology | en_US |
dc.publication | Peer Reviewed | en_US |
dc.identifier.doi | 10.3390/electronics8040420 | en_US |
dc.relation.issue | 4 | en_US |
dc.relation.volume | 8 | en_US |
cut.common.academicyear | 2018-2019 | en_US |
item.fulltext | With Fulltext | - |
item.languageiso639-1 | en | - |
item.grantfulltext | open | - |
item.openairecristype | http://purl.org/coar/resource_type/c_6501 | - |
item.cerifentitytype | Publications | - |
item.openairetype | article | - |
crisitem.journal.journalissn | 2079-9292 | - |
crisitem.journal.publisher | MDPI | - |
crisitem.author.dept | Department of Civil Engineering and Geomatics | - |
crisitem.author.faculty | Faculty of Engineering and Technology | - |
crisitem.author.orcid | 0000-0002-2016-9079 | - |
crisitem.author.parentorg | Faculty of Engineering and Technology | - |
Appears in Collections: | Άρθρα/Articles |
Files in This Item:
File | Description | Size | Format | |
---|---|---|---|---|
electronics-08-00420-v2.pdf | Fulltext | 14.27 MB | Adobe PDF | View/Open |
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