Please use this identifier to cite or link to this item: https://hdl.handle.net/20.500.14279/14196
Title: Implementation of pattern recognition algorithms in processing incomplete wind speed data for energy assessment of offshore wind turbines
Authors: Panapakidis, Ioannis P. 
Michailides, Constantine 
Angelides, Demos C. 
Major Field of Science: Natural Sciences
Field Category: Computer and Information Sciences
Keywords: Incomplete data;Missing data;Offshore wind turbines;Time series clustering;Unsupervised machine learning;Wind speed
Issue Date: Apr-2019
Source: Electronics, 2019, vol. 8, no. 4
Volume: 8
Issue: 4
Journal: Electronics 
Abstract: Offshore wind turbine (OWT) installations are continually expanding as they are considered an efficient mechanism for covering a part of the energy consumption requirements. The assessment of the energy potential of OWTs for specific offshore sites is the key factor that defines their successful implementation, commercialization and sustainability. The data used for this assessment mainly refer to wind speed measurements. However, the data may not present homogeneity due to incomplete or missing entries; this in turn, is attributed to failures of the measuring devices or other factors. This fact may lead to considerable limitations in the OWTs energy potential assessment. This paper presents two novel methodologies to handle the problem of incomplete and missing data. Computational intelligence algorithms are utilized for the filling of the incomplete and missing data in order to build complete wind speed series. Finally, the complete wind speed series are used for assessing the energy potential of an OWT in a specific offshore site. In many real-world metering systems, due to meter failures, incomplete and missing data are frequently observed, leading to the need for robust data handling. The novelty of the paper can be summarized in the following points: (i) a comparison of clustering algorithms for extracting typical wind speed curves is presented for the OWT related literature and (ii) two efficient novel methods for missing and incomplete data are proposed.
ISSN: 20799292
DOI: 10.3390/electronics8040418
Rights: © the authors. Licensee MDPI, Basel, Switzerland.
Type: Article
Affiliation : University of Thessaly 
Cyprus University of Technology 
Aristotle University of Thessaloniki 
Publication Type: Peer Reviewed
Appears in Collections:Άρθρα/Articles

Files in This Item:
File Description SizeFormat
electronics-08-00418.pdfFulltext15.57 MBAdobe PDFView/Open
CORE Recommender
Show full item record

SCOPUSTM   
Citations

4
checked on Nov 6, 2023

WEB OF SCIENCETM
Citations

2
Last Week
0
Last month
0
checked on Oct 29, 2023

Page view(s)

416
Last Week
0
Last month
7
checked on Nov 6, 2024

Download(s)

113
checked on Nov 6, 2024

Google ScholarTM

Check

Altmetric


Items in KTISIS are protected by copyright, with all rights reserved, unless otherwise indicated.