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Outdoor PV system monitoring—input data quality, data imputation and filtering approaches
ID
Lindig, Sascha
(
Author
),
ID
Louwen, Atse
(
Author
),
ID
Moser, David
(
Author
),
ID
Topič, Marko
(
Author
)
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MD5: EC7F59F6D17DCF8BCBBB0EDE3F0D4F3C
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https://www.mdpi.com/1996-1073/13/19/5099
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Abstract
Photovoltaic monitoring data are the primary source for studying photovoltaic plant behavior. In particular, performance loss and remaining-useful-lifetime calculations rely on trustful input data. Furthermore, a regular stream of high quality is the basis for pro-active operation and management activities which ensure a smooth operation of PV plants. The raw data under investigation are electrical measurements and usually meteorological data such as in-plane irradiance and temperature. Usually, performance analyses follow a strict pattern of checking input data quality followed by the application of appropriate filter, choosing a key performance indicator and the application of certain methodologies to receive a final result. In this context, this paper focuses on four main objectives. We present common photovoltaics monitoring data quality issues, provide visual guidelines on how to detect and evaluate these, provide new data imputation approaches, and discuss common filtering approaches. Data imputation techniques for module temperature and irradiance data are discussed and compared to classical approaches. This work is intended to be a soft introduction into PV monitoring data analysis discussing best practices and issues an analyst might face. It was seen that if a sufficient amount of training data is available, multivariate adaptive regression splines yields good results for module temperature imputation while histogram-based gradient boosting regression outperforms classical approaches for in-plane irradiance transposition. Based on tested filtering procedures, it is believed that standards should be developed including relatively low irradiance thresholds together with strict power-irradiance pair filters.
Language:
English
Keywords:
photovoltaics
,
solar cells
,
photovoltaic system performance
,
photovoltaic system data
,
data quality
,
data imputation
,
data filtering
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
FE - Faculty of Electrical Engineering
Publication status:
Published
Publication version:
Version of Record
Year:
2020
Number of pages:
18 str.
Numbering:
Vol. 13, iss. 19, art. 5099
PID:
20.500.12556/RUL-134484
UDC:
621.383.51
ISSN on article:
1996-1073
DOI:
10.3390/en13195099
COBISS.SI-ID:
32452611
Publication date in RUL:
18.01.2022
Views:
1187
Downloads:
168
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Record is a part of a journal
Title:
Energies
Shortened title:
Energies
Publisher:
Molecular Diversity Preservation International
ISSN:
1996-1073
COBISS.SI-ID:
518046745
Licences
License:
CC BY 4.0, Creative Commons Attribution 4.0 International
Link:
http://creativecommons.org/licenses/by/4.0/
Description:
This is the standard Creative Commons license that gives others maximum freedom to do what they want with the work as long as they credit the author.
Licensing start date:
01.10.2020
Secondary language
Language:
Slovenian
Keywords:
fotovoltaika
,
sončne celice
Projects
Funder:
EC - European Commission
Funding programme:
H2020
Project number:
721452
Name:
Photovoltaic module life time forecast and evaluation
Acronym:
SOLAR-TRAIN
Funder:
EC - European Commission
Funding programme:
European Regional Development Fund
Funder:
Other - Other funder or multiple funders
Funding programme:
Autonomous Province Bolzano—South Tyrol
Project number:
FESR1128-Project PV4.0
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