Details
| Originalsprache | Englisch |
|---|---|
| Titel des Sammelwerks | 2021 IEEE 48th Photovoltaic Specialists Conference |
| Untertitel | PVSC 2021 |
| Herausgeber (Verlag) | Institute of Electrical and Electronics Engineers Inc. |
| Seiten | 294-299 |
| Seitenumfang | 6 |
| ISBN (elektronisch) | 9781665419222 |
| Publikationsstatus | Veröffentlicht - 2021 |
| Veranstaltung | 48th IEEE Photovoltaic Specialists Conference, PVSC 2021 - Fort Lauderdale, USA / Vereinigte Staaten Dauer: 20 Juni 2021 → 25 Juni 2021 |
Publikationsreihe
| Name | Conference Record of the IEEE Photovoltaic Specialists Conference |
|---|---|
| ISSN (Print) | 0160-8371 |
Abstract
The accurate calculation of annual insolation for a large amount of building surfaces in urban environments is a key prerequisite for a targeted increase of the installed PV capacity. In this study, we build a digital city model upon publicly available geographic data. We use this model in combination with machine learning for performing PV potential analyses on large scales and at low computational costs. We train the machine learning algorithm using ground truth values for insolation, which we determine from forward ray tracing calculations for the city of Hanover, Germany. We find that our machine learning approach is able to predict the annual insolation for all types of building surfaces independent from their tilt and orientation. The RMSE relative to the mean ground truth value is 3.2% for rooftops and 8.8% for facades. The calculation time is reduced by a factor of 20 compared to the forward ray tracing approach.
ASJC Scopus Sachgebiete
- Ingenieurwesen (insg.)
- Steuerungs- und Systemtechnik
- Ingenieurwesen (insg.)
- Wirtschaftsingenieurwesen und Fertigungstechnik
- Ingenieurwesen (insg.)
- Elektrotechnik und Elektronik
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- BibTex
- RIS
2021 IEEE 48th Photovoltaic Specialists Conference: PVSC 2021. Institute of Electrical and Electronics Engineers Inc., 2021. S. 294-299 (Conference Record of the IEEE Photovoltaic Specialists Conference).
Publikation: Beitrag in Buch/Bericht/Sammelwerk/Konferenzband › Aufsatz in Konferenzband › Forschung › Peer-Review
}
TY - GEN
T1 - Fast Evaluation of Rooftop and Façade PV Potentials Using Backward Ray Tracing and Machine Learning
AU - Bredemeier, Dennis
AU - Schinke, Carsten
AU - Gewohn, Timo
AU - Wagner-Mohnsen, Hannes
AU - Niepelt, Raphael
AU - Brendel, Rolf
N1 - Funding Information: This work is funded by the Lower Saxony Ministry of Science and Culture (MWK, grant number 74ZN1596 ‘Modelling the Energy System Transformation’) Funding Information: ACKNOWLEDGMENT This work is supported by the compute cluster, which is funded by the Leibniz Universität Hannover, the Lower Saxony Ministry of Science and Culture (MWK) and the German Research Association (DFG).
PY - 2021
Y1 - 2021
N2 - The accurate calculation of annual insolation for a large amount of building surfaces in urban environments is a key prerequisite for a targeted increase of the installed PV capacity. In this study, we build a digital city model upon publicly available geographic data. We use this model in combination with machine learning for performing PV potential analyses on large scales and at low computational costs. We train the machine learning algorithm using ground truth values for insolation, which we determine from forward ray tracing calculations for the city of Hanover, Germany. We find that our machine learning approach is able to predict the annual insolation for all types of building surfaces independent from their tilt and orientation. The RMSE relative to the mean ground truth value is 3.2% for rooftops and 8.8% for facades. The calculation time is reduced by a factor of 20 compared to the forward ray tracing approach.
AB - The accurate calculation of annual insolation for a large amount of building surfaces in urban environments is a key prerequisite for a targeted increase of the installed PV capacity. In this study, we build a digital city model upon publicly available geographic data. We use this model in combination with machine learning for performing PV potential analyses on large scales and at low computational costs. We train the machine learning algorithm using ground truth values for insolation, which we determine from forward ray tracing calculations for the city of Hanover, Germany. We find that our machine learning approach is able to predict the annual insolation for all types of building surfaces independent from their tilt and orientation. The RMSE relative to the mean ground truth value is 3.2% for rooftops and 8.8% for facades. The calculation time is reduced by a factor of 20 compared to the forward ray tracing approach.
KW - machine learning
KW - PV potential analysis
KW - ray tracing
UR - http://www.scopus.com/inward/record.url?scp=85115951483&partnerID=8YFLogxK
U2 - 10.1109/PVSC43889.2021.9518660
DO - 10.1109/PVSC43889.2021.9518660
M3 - Conference contribution
AN - SCOPUS:85115951483
T3 - Conference Record of the IEEE Photovoltaic Specialists Conference
SP - 294
EP - 299
BT - 2021 IEEE 48th Photovoltaic Specialists Conference
PB - Institute of Electrical and Electronics Engineers Inc.
T2 - 48th IEEE Photovoltaic Specialists Conference, PVSC 2021
Y2 - 20 June 2021 through 25 June 2021
ER -