TY - JOUR
T1 - A density‐based time‐series data analysis methodology for shadow detection in rooftop photovoltaic systems
AU - Tsafarakis, Odysseas
AU - Sark, Wilfried G.J.H.M. van
N1 - Funding Information:
The authors gratefully acknowledge fruitful discussions with Kostas Sinapis (TNO) and Lex Schiebaan (Sundata) and Guido van Sark for providing the 3D system representation shown in Figure 2 . This work is partly financially supported by the Netherlands Enterprise Agency (RVO) within the framework of the Dutch Topsector Energy (project Intelligent Health Assessment of PV Systems, IHAPS, grant number TEUE117067).
Funding Information:
The authors gratefully acknowledge fruitful discussions with Kostas Sinapis (TNO) and Lex Schiebaan (Sundata) and Guido van Sark for providing the 3D system representation shown in Figure 2. This work is partly financially supported by the Netherlands Enterprise Agency (RVO) within the framework of the Dutch Topsector Energy (project Intelligent Health Assessment of PV Systems, IHAPS, grant number TEUE117067).
Publisher Copyright:
© 2022 The Authors. Progress in Photovoltaics: Research and Applications published by John Wiley & Sons Ltd.
PY - 2023/5
Y1 - 2023/5
N2 - The majority of photovoltaic (PV) systems in the Netherlands are small scale, installed on rooftops, where the lack of onsite global tilted irradiance (GTI) measurements and the frequent presence of shadow due to objects in the close vicinity oppose challenge in their monitoring process. In this study, a new algorithmic tool is introduced that creates a reference data-set through the combination of data-sets of the unshaded PV systems in the surrounding area. It subsequently compares the created reference data-set with the one of the PV system of interest, detects any energy loss and clusters the distinctive loss due to shadow, created by the surrounding objects. The new algorithm is applied successfully to a number of different cases of shaded PV systems. Finally, suggestions on the unsupervised use of the algorithm by any monitoring platform are discussed, along with its limitations algorithm and suggestions for further research.
AB - The majority of photovoltaic (PV) systems in the Netherlands are small scale, installed on rooftops, where the lack of onsite global tilted irradiance (GTI) measurements and the frequent presence of shadow due to objects in the close vicinity oppose challenge in their monitoring process. In this study, a new algorithmic tool is introduced that creates a reference data-set through the combination of data-sets of the unshaded PV systems in the surrounding area. It subsequently compares the created reference data-set with the one of the PV system of interest, detects any energy loss and clusters the distinctive loss due to shadow, created by the surrounding objects. The new algorithm is applied successfully to a number of different cases of shaded PV systems. Finally, suggestions on the unsupervised use of the algorithm by any monitoring platform are discussed, along with its limitations algorithm and suggestions for further research.
KW - cluster analysis
KW - density-based spatial clustering of applications with noise (DBSCAN)
KW - malfunction detection
KW - monitoring
KW - photovoltaic systems
KW - shadow detection
UR - http://www.scopus.com/inward/record.url?scp=85143992067&partnerID=8YFLogxK
U2 - 10.1002/pip.3654
DO - 10.1002/pip.3654
M3 - Article
SN - 1062-7995
VL - 31
SP - 506
EP - 523
JO - Progress in Photovoltaics: Research and Applications
JF - Progress in Photovoltaics: Research and Applications
IS - 5
ER -