<div class="csl-bib-body">
<div class="csl-entry">David, A., Sint, S., & Bednar, T. (2023). Data-Driven Approach Utilising Random Forest Regression for PV Performance Monitoring. In International Solar Energy Society (Ed.), <i>Proceedings of EuroSun 2022 - ISES and IEA SHC International Conference on Solar Energy for Buildings and Industry</i> (pp. 1699–1710). https://doi.org/10.18086/eurosun.2022.16.03</div>
</div>
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dc.identifier.uri
http://hdl.handle.net/20.500.12708/193115
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dc.description.abstract
The performance monitoring of PV plants is essential to ensure their correct operation, detect faults, and maximise their output. To be able to decide whether a plant is operating within normal parameters, a reference is needed, which indicates the PV performance that the plant should have under certain weather conditions. Weather data and historical monitoring data from times when the PV plant was operating correctly can be used to train machine learning models, which can provide the reference PV performance. In this research, a random forest regression machine learning algorithm is used to train models which predict the electrical power that is measured by 19 PV inverters and the PV main electricity meter. Several random forest models utilising different parts of the available weather data were trained. Their prediction performance was evaluated for five different time resolutions and three different PV module orientations. The results indicate that random forest regression is a suitable tool to predict the performance of PV plants.
en
dc.description.sponsorship
ARGE Kratochwil-Waldbauer & Zeinitz
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dc.language.iso
en
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dc.subject
photovoltaic
en
dc.subject
performance monitoring
en
dc.subject
performance prediction
en
dc.subject
machine learning
en
dc.subject
random forest regression
en
dc.title
Data-Driven Approach Utilising Random Forest Regression for PV Performance Monitoring
en
dc.type
Inproceedings
en
dc.type
Konferenzbeitrag
de
dc.relation.isbn
978-3-982 0408-8-2
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dc.description.startpage
1699
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dc.description.endpage
1710
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dc.type.category
Full-Paper Contribution
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tuw.booktitle
Proceedings of EuroSun 2022 - ISES and IEA SHC International Conference on Solar Energy for Buildings and Industry
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tuw.peerreviewed
true
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tuw.project.title
Inbetriebnahme und Optimierung Österreichs größtes Plus-Energie-Bürogebäude am Standort Getreidemarkt der TU Wien
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tuw.researchTopic.id
E1
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tuw.researchTopic.id
C6
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tuw.researchTopic.name
Energy Active Buildings, Settlements and Spatial Infrastructures
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tuw.researchTopic.name
Modeling and Simulation
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tuw.researchTopic.value
50
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tuw.researchTopic.value
50
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tuw.publication.orgunit
E207-02 - Forschungsbereich Bauphysik
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tuw.publisher.doi
10.18086/eurosun.2022.16.03
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dc.description.numberOfPages
12
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tuw.author.orcid
0000-0001-8076-8228
-
tuw.author.orcid
0000-0003-1142-5517
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tuw.event.name
EuroSun 2022
en
tuw.event.startdate
25-09-2022
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tuw.event.enddate
29-09-2022
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tuw.event.online
On Site
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tuw.event.type
Event for scientific audience
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tuw.event.place
Kassel
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tuw.event.country
DE
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tuw.event.presenter
David, Alexander
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tuw.event.track
Multi Track
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wb.sciencebranch
Bauingenieurwesen
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wb.sciencebranch.oefos
2011
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wb.sciencebranch.value
100
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item.openairecristype
http://purl.org/coar/resource_type/c_5794
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item.cerifentitytype
Publications
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item.grantfulltext
restricted
-
item.fulltext
no Fulltext
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item.languageiso639-1
en
-
item.openairetype
conference paper
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crisitem.project.funder
ARGE Kratochwil-Waldbauer & Zeinitz
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crisitem.author.dept
E207-02 - Forschungsbereich Bauphysik
-
crisitem.author.dept
E207-02 - Forschungsbereich Bauphysik
-
crisitem.author.dept
E207 - Institut für Werkstofftechnologie, Bauphysik und Bauökologie
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crisitem.author.orcid
0000-0001-8076-8228
-
crisitem.author.orcid
0000-0003-1142-5517
-
crisitem.author.parentorg
E207 - Institut für Werkstofftechnologie, Bauphysik und Bauökologie
-
crisitem.author.parentorg
E207 - Institut für Werkstofftechnologie, Bauphysik und Bauökologie