Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind Turbine

In this work, a cost-effective wind resource method specifically developed for the ROSEO-BIWT (Building Integrated Wind Turbine) and other Building Integrated Wind Turbines is presented. It predicts the wind speed and direction at the roof of an previously selected building for the past 10 years usi...

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Autores: González Arceo, Andoni, Zirion Martínez de Musitu, Maitane, Ulazia Manterola, Alain, Del Rio Rodríguez, Mario, García, Oscar
Tipo de recurso: artículo
Fecha de publicación:2020
País:España
Institución:Universidad del País Vasco
Repositorio:Addi. Archivo Digital para la Docencia y la Investigación
OAI Identifier:oai:addi.ehu.eus:10810/49685
Acceso en línea:http://hdl.handle.net/10810/49685
Access Level:acceso abierto
Palabra clave:building integrated wind turbines
reanalysis
ERA5
anemometer
Savonious turbine
quantile mapping
random forest
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spelling Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind TurbineGonzález Arceo, AndoniZirion Martínez de Musitu, MaitaneUlazia Manterola, AlainDel Rio Rodríguez, MarioGarcía, Oscarbuilding integrated wind turbinesreanalysisERA5anemometerSavonious turbinequantile mappingrandom forestIn this work, a cost-effective wind resource method specifically developed for the ROSEO-BIWT (Building Integrated Wind Turbine) and other Building Integrated Wind Turbines is presented. It predicts the wind speed and direction at the roof of an previously selected building for the past 10 years using reanalysis data and wind measurements taken over a year. To do so, the reanalysis wind speed data is calibrated against the measurements using different kinds of quantile mapping, and the wind direction is predicted using random forest. A mock-up of a building and a BIWT were used in a wind tunnel to perform a small-scale experiment presented here. It showed that energy production is possible and even enhanced over a wide range of attack angles. The energy production estimations made with the best performing kind of calibration achieved an overall relative error of 6.77% across different scenarios.This research was funded by the University of the Basque Country (UPV/EHU) GIU 17/002, and the Basque Autonomous Government’s BEAZ-SPRI grant for the creation of innovative enterprises (ROSEO EOLICA URBANA).MDPI2021202120202020info:eu-repo/semantics/articleapplication/pdfhttp://hdl.handle.net/10810/49685reponame:Addi. Archivo Digital para la Docencia y la Investigacióninstname:Universidad del País VascoIngléshttps://www.mdpi.com/2076-3417/10/24/9017/htminfo:eu-repo/semantics/openAccesshttp://creativecommons.org/licenses/by/3.0/es/2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).oai:addi.ehu.eus:10810/496852026-06-18T09:23:17Z
dc.title.none.fl_str_mv Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind Turbine
title Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind Turbine
spellingShingle Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind Turbine
González Arceo, Andoni
building integrated wind turbines
reanalysis
ERA5
anemometer
Savonious turbine
quantile mapping
random forest
title_short Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind Turbine
title_full Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind Turbine
title_fullStr Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind Turbine
title_full_unstemmed Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind Turbine
title_sort Calibration of Reanalysis Data against Wind Measurements for Energy Production Estimation of Building Integrated Savonius-Type Wind Turbine
dc.creator.none.fl_str_mv González Arceo, Andoni
Zirion Martínez de Musitu, Maitane
Ulazia Manterola, Alain
Del Rio Rodríguez, Mario
García, Oscar
author González Arceo, Andoni
author_facet González Arceo, Andoni
Zirion Martínez de Musitu, Maitane
Ulazia Manterola, Alain
Del Rio Rodríguez, Mario
García, Oscar
author_role author
author2 Zirion Martínez de Musitu, Maitane
Ulazia Manterola, Alain
Del Rio Rodríguez, Mario
García, Oscar
author2_role author
author
author
author
dc.subject.none.fl_str_mv building integrated wind turbines
reanalysis
ERA5
anemometer
Savonious turbine
quantile mapping
random forest
topic building integrated wind turbines
reanalysis
ERA5
anemometer
Savonious turbine
quantile mapping
random forest
description In this work, a cost-effective wind resource method specifically developed for the ROSEO-BIWT (Building Integrated Wind Turbine) and other Building Integrated Wind Turbines is presented. It predicts the wind speed and direction at the roof of an previously selected building for the past 10 years using reanalysis data and wind measurements taken over a year. To do so, the reanalysis wind speed data is calibrated against the measurements using different kinds of quantile mapping, and the wind direction is predicted using random forest. A mock-up of a building and a BIWT were used in a wind tunnel to perform a small-scale experiment presented here. It showed that energy production is possible and even enhanced over a wide range of attack angles. The energy production estimations made with the best performing kind of calibration achieved an overall relative error of 6.77% across different scenarios.
publishDate 2020
dc.date.none.fl_str_mv 2020
2020
2021
2021
dc.type.none.fl_str_mv info:eu-repo/semantics/article
format article
dc.identifier.none.fl_str_mv http://hdl.handle.net/10810/49685
url http://hdl.handle.net/10810/49685
dc.language.none.fl_str_mv Inglés
language_invalid_str_mv Inglés
dc.relation.none.fl_str_mv https://www.mdpi.com/2076-3417/10/24/9017/htm
dc.rights.none.fl_str_mv info:eu-repo/semantics/openAccess
http://creativecommons.org/licenses/by/3.0/es/
eu_rights_str_mv openAccess
rights_invalid_str_mv http://creativecommons.org/licenses/by/3.0/es/
dc.format.none.fl_str_mv application/pdf
dc.publisher.none.fl_str_mv MDPI
publisher.none.fl_str_mv MDPI
dc.source.none.fl_str_mv reponame:Addi. Archivo Digital para la Docencia y la Investigación
instname:Universidad del País Vasco
instname_str Universidad del País Vasco
reponame_str Addi. Archivo Digital para la Docencia y la Investigación
collection Addi. Archivo Digital para la Docencia y la Investigación
repository.name.fl_str_mv
repository.mail.fl_str_mv
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score 15.301603

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