Publication
Evaluation of MCP Correlation Algorithms Applied to Wind Data Series
| datacite.subject.fos | Engenharia e Tecnologia | |
| datacite.subject.sdg | 09:Indústria, Inovação e Infraestruturas | |
| dc.contributor.author | Moreira, A. | |
| dc.contributor.author | Rocha, T. | |
| dc.contributor.author | Mendonça, J. | |
| dc.contributor.author | Pilão, R. | |
| dc.contributor.author | Pinto, P. | |
| dc.date.accessioned | 2025-02-17T19:09:40Z | |
| dc.date.available | 2025-02-17T19:09:40Z | |
| dc.date.issued | 2024-06-26 | |
| dc.description.abstract | This work aimed to develop methodologies for analysing statistical correlations among wind data series using various Measure-Correlate-Predict (MCP) methods, with the goal of selecting the most suitable method for extrapolating long-term data with minimal associated uncertainty. It was analysed the minimum time required for a wind measurement campaign when applying this methodology. Fifteen local wind measurement stations were selected. The long-term wind data reanalysis series that exhibited the strongest correlation with the measured wind data at each station was then chosen. Multiple tests were conducted with different simultaneous periods between the measured data series and the long-term series. Fifteen correlation algorithms were tested for each concurrent period. The performance of each model was evaluated using the RMSE (Root Mean Square Error) and MBE (Mean Bias Error) associated with each MCP. Analysis of the errors identified measurement periods with the lowest associated error ranging from 1 to 5 years and a single-factor ANOVA analysis was conducted. Finally, t-significance tests were performed. The study concluded that the Neural Network was the most effective MCP method. Additionally, it was determined that the minimum number of years required for a local measurement campaign should be between 2 and 3 years. | eng |
| dc.identifier.citation | Moreira, A., Rocha, T., Mendonça, J., Pilão, R. & Pinto, P. (2024, June 26-28) Evaluation of MCP Correlation Algorithms Applied to Wind Data Series [Paper presentation]. 22nd International Conference on Renewable Energies and Power Quality (ICREPQ´24), Bilbao, Spain. https://doi.org/10.52152/4060 | |
| dc.identifier.doi | 10.52152/4060 | |
| dc.identifier.issn | 2172-038X | |
| dc.identifier.uri | http://hdl.handle.net/10400.22/29547 | |
| dc.language.iso | eng | |
| dc.peerreviewed | yes | |
| dc.publisher | Renewable Energies & Power Quality Journal | |
| dc.relation | Center for Innovation in Industrial Engineering and Technology | |
| dc.relation.hasversion | https://repqj.com/index.php/repqj/article/view/4060 | |
| dc.rights.uri | N/A | |
| dc.subject | MCP | |
| dc.subject | correlation algorithms | |
| dc.subject | local measurement station | |
| dc.subject | wind regime | |
| dc.subject | reanalysis data series | |
| dc.title | Evaluation of MCP Correlation Algorithms Applied to Wind Data Series | eng |
| dc.type | conference paper | |
| dspace.entity.type | Publication | |
| oaire.awardTitle | Center for Innovation in Industrial Engineering and Technology | |
| oaire.awardURI | info:eu-repo/grantAgreement/FCT/6817 - DCRRNI ID/UIDB%2F04730%2F2020/PT | |
| oaire.citation.conferenceDate | 2024-06-26 | |
| oaire.citation.conferencePlace | Bilbao, Spain | |
| oaire.citation.issue | 6 | |
| oaire.citation.title | 22nd International Conference on Renewable Energies and Power Quality (ICREPQ´24) | |
| oaire.citation.volume | 22 | |
| oaire.fundingStream | 6817 - DCRRNI ID | |
| oaire.version | http://purl.org/coar/version/c_970fb48d4fbd8a85 | |
| project.funder.identifier | http://doi.org/10.13039/501100001871 | |
| project.funder.name | Fundação para a Ciência e a Tecnologia | |
| relation.isProjectOfPublication | cdbfce2f-6ff0-4d59-a7c6-96c99d52a570 | |
| relation.isProjectOfPublication.latestForDiscovery | cdbfce2f-6ff0-4d59-a7c6-96c99d52a570 |