Statistical Analysis of Solar Irradiance Variability
Statistical Analysis of Solar Irradiance Variability
Solar photovoltaic (PV) generation forecasting is an important tool to power system operators, but struggles under conditions of intermittent solar irradiance. Although studying and forecasting irradiance itself has been the subject of much
research, little progress has been made on the variability (or fluctuation) of irradiance and its statistical properties, despite it being an important parameter in generation forecasting, state estimation and other power system applications. This paper takes a close look into the statistical nature of irradiance variability and shows that it can be sufficiently modeled by a Gaussian Mixture Model (GMM) of six components. Furthermore, an investigation on the required time resolution demonstrates that sub-minute resolution is necessary to accurately capture irradiance variability.
The analysis is performed on a one-second resolution irradiance
dataset provided by NREL.
Gaussian Mixture Model (GMM), RR, solar irradiance, photovoltaic (PV) forecasting
Nikolopoulos, Angelos R.
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Batzelis, Efstratios I.
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Lewin, Paul
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Nikolaou, Nikolaos
ed61ff7c-4b80-408b-b8de-f0d38f148a76
21 July 2024
Nikolopoulos, Angelos R.
3a6d9258-9a8f-47f2-8593-083eb9f0f6a3
Batzelis, Efstratios I.
2a85086e-e403-443c-81a6-e3b4ee16ae5e
Lewin, Paul
78b4fc49-1cb3-4db9-ba90-3ae70c0f639e
Nikolaou, Nikolaos
ed61ff7c-4b80-408b-b8de-f0d38f148a76
Nikolopoulos, Angelos R., Batzelis, Efstratios I., Lewin, Paul and Nikolaou, Nikolaos
(2024)
Statistical Analysis of Solar Irradiance Variability.
2024 IEEE Power & Energy Society General Meeting, Summit - Seattle Convention Center, Seattle, United States.
21 - 25 Jul 2024.
5 pp
.
Record type:
Conference or Workshop Item
(Paper)
Abstract
Solar photovoltaic (PV) generation forecasting is an important tool to power system operators, but struggles under conditions of intermittent solar irradiance. Although studying and forecasting irradiance itself has been the subject of much
research, little progress has been made on the variability (or fluctuation) of irradiance and its statistical properties, despite it being an important parameter in generation forecasting, state estimation and other power system applications. This paper takes a close look into the statistical nature of irradiance variability and shows that it can be sufficiently modeled by a Gaussian Mixture Model (GMM) of six components. Furthermore, an investigation on the required time resolution demonstrates that sub-minute resolution is necessary to accurately capture irradiance variability.
The analysis is performed on a one-second resolution irradiance
dataset provided by NREL.
Text
AN_2024_PESGM_Accepted_Manuscript
- Accepted Manuscript
More information
Accepted/In Press date: 21 February 2024
Published date: 21 July 2024
Venue - Dates:
2024 IEEE Power & Energy Society General Meeting, Summit - Seattle Convention Center, Seattle, United States, 2024-07-21 - 2024-07-25
Keywords:
Gaussian Mixture Model (GMM), RR, solar irradiance, photovoltaic (PV) forecasting
Identifiers
Local EPrints ID: 487616
URI: http://eprints.soton.ac.uk/id/eprint/487616
PURE UUID: bb796a85-52b6-4a6f-8f8d-e988438a84ca
Catalogue record
Date deposited: 29 Feb 2024 17:37
Last modified: 18 Mar 2024 04:01
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Contributors
Author:
Angelos R. Nikolopoulos
Author:
Efstratios I. Batzelis
Author:
Paul Lewin
Author:
Nikolaos Nikolaou
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