Data-driven modelling of the irradiance and temperature effect on the photovoltaic model parameters
Data-driven modelling of the irradiance and temperature effect on the photovoltaic model parameters
The irradiance and temperature impact on the photovoltaic(PV) model has been extensively modelled in the past using analytical physics-based expressions. However,such methods tend to under perform under low irradiance conditions and certain PV technologies. This paper explores the potential of four machine learning (ML) alternatives on this task, i.e. Neural Networks, Support Vector Machine, K Nearest Neighbours, and Extreme Gradient Boosting. This analysis is performed on datasets of mono- and poly- crystalline PV modules provided by NREL. The findings demonstrate that ML methods generally outperform the widely adopted analytical approach, particularly in the parasitic resistances representation. Extreme Gradient Boosting is found to be the front-runner, while the results also indicate potential for transferability of models trained on one PV module to another of the same PV technology.
photovoltaic(PV), single diode model, five parameters, Machine Learning, transferability
Nikolopoulos, Angelos R.
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Batzelis, Efstratios
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Lewin, Paul
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Nikolopoulos, Angelos R.
3a6d9258-9a8f-47f2-8593-083eb9f0f6a3
Batzelis, Efstratios
2a85086e-e403-443c-81a6-e3b4ee16ae5e
Lewin, Paul
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Nikolopoulos, Angelos R., Batzelis, Efstratios and Lewin, Paul
(2024)
Data-driven modelling of the irradiance and temperature effect on the photovoltaic model parameters.
In Data-driven modelling of the irradiance and temperature effect on the photovoltaic model parameters.
Springer.
6 pp
.
(In Press)
Record type:
Conference or Workshop Item
(Paper)
Abstract
The irradiance and temperature impact on the photovoltaic(PV) model has been extensively modelled in the past using analytical physics-based expressions. However,such methods tend to under perform under low irradiance conditions and certain PV technologies. This paper explores the potential of four machine learning (ML) alternatives on this task, i.e. Neural Networks, Support Vector Machine, K Nearest Neighbours, and Extreme Gradient Boosting. This analysis is performed on datasets of mono- and poly- crystalline PV modules provided by NREL. The findings demonstrate that ML methods generally outperform the widely adopted analytical approach, particularly in the parasitic resistances representation. Extreme Gradient Boosting is found to be the front-runner, while the results also indicate potential for transferability of models trained on one PV module to another of the same PV technology.
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Accepted/In Press date: 5 March 2024
Venue - Dates:
15th International Conference on Modeling and Simulation of Electric Machines, Converters and Systems, Doctoral School and Board of Trustees' Office building, Riu Sec Campus of the Universitat Jaume I, Castelló de la Plana, Spain, 2024-05-27 - 2024-05-30
Keywords:
photovoltaic(PV), single diode model, five parameters, Machine Learning, transferability
Identifiers
Local EPrints ID: 487832
URI: http://eprints.soton.ac.uk/id/eprint/487832
PURE UUID: 0dafaf1f-87ab-4b26-9ba1-827931518c64
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Date deposited: 06 Mar 2024 17:31
Last modified: 18 Mar 2024 04:01
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Contributors
Author:
Angelos R. Nikolopoulos
Author:
Efstratios Batzelis
Author:
Paul Lewin
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