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Advanced PV performance modelling based on different levels of irradiance data accuracy
ID
Ascencio Vásquez, Julián Andrés
(
Author
),
ID
Bevc, Jakob
(
Author
),
ID
Reba, Kristjan
(
Author
),
ID
Brecl, Kristijan
(
Author
),
ID
Jankovec, Marko
(
Author
),
ID
Topič, Marko
(
Author
)
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https://www.mdpi.com/1996-1073/13/9/2166/
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Abstract
In photovoltaic (PV) systems, energy yield is one of the essential pieces of information to the stakeholders (grid operators, maintenance operators, financial units, etc.). The amount of energy produced by a photovoltaic system in a specific time period depends on the weather conditions, including snow and dust, the actual PV modules’ and inverters’ efficiency and balance-of-system losses. The energy yield can be estimated by using empirical models with accurate input data. However, most of the PV systems do not include on-site high-class measurement devices for irradiance and other weather conditions. For this reason, the use of reanalysis-based or satellite-based data is currently of significant interest in the PV community and combining the data with decomposition and transposition irradiance models, the actual Plane-of-Array operating conditions can be determined. In this paper, we are proposing an efficient and accurate approach for PV output energy modelling by combining a new data filtering procedure and fast machine learning algorithm Light Gradient Boosting Machine (LightGBM). The applicability of the procedure is presented on three levels of irradiance data accuracy (low, medium, and high) depending on the source or modelling used. A new filtering algorithm is proposed to exclude erroneous data due to system failures or unreal weather conditions (i.e., shading, partial snow coverage, reflections, soiling deposition, etc.). The cleaned data is then used to train three empirical models and three machine learning approaches, where we emphasize the advantages of the LightGBM. The experiments are carried out on a 17 kW roof-top PV system installed in Ljubljana, Slovenia, in a temperate climate zone.
Language:
English
Keywords:
PV performance modelling
,
data filtering
,
PV systems
,
machine learning
,
lightGBM
Work type:
Article
Typology:
1.01 - Original Scientific Article
Organization:
FE - Faculty of Electrical Engineering
FRI - Faculty of Computer and Information Science
Publication status:
Published
Publication version:
Version of Record
Year:
2020
Number of pages:
12 str.
Numbering:
Vol. 13, iss. 9, art. 2166
PID:
20.500.12556/RUL-115985
UDC:
621.383.51
ISSN on article:
1996-1073
DOI:
10.3390/en13092166
COBISS.SI-ID:
13529091
Publication date in RUL:
05.05.2020
Views:
1081
Downloads:
436
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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:
01.05.2020
Secondary language
Language:
Slovenian
Keywords:
modeliranje učinkovitosti PV
,
filtriranje podatkov
,
PV sistemi
,
strojno učenje
,
lightGMB
Projects
Funder:
EC - European Commission
Funding programme:
H2020
Project number:
721452
Name:
Photovoltaic module life time forecast and evaluation
Acronym:
SOLAR-TRAIN
Funder:
ARRS - Slovenian Research Agency
Project number:
P2-0197
Name:
Fotovoltaika in elektronika
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