The University of Southampton
University of Southampton Institutional Repository

Development and validation of optical models for predicting solar irradiance in temperate climates

Development and validation of optical models for predicting solar irradiance in temperate climates
Development and validation of optical models for predicting solar irradiance in temperate climates
As the global energy landscape shifts toward renewable sources, particularly photovoltaic (PV) technology, PV installations are expanding rapidly, covering larger areas and integrating advanced innovations. This growth poses new challenges for feasibility software used to evaluate solar projects, especially as diverse regions adopt emerging PV technologies. Accurate solar irradiance prediction plays a crucial role in ensuring the reliability of feasibility assessments and financial forecasting, as these predictions form the backbone of feasibility software's performance. This research delves into advanced PV technologies, including bifacial panels, tracking systems, and AgriPhotovoltaics (AgriPV), focusing on their behavior under the dynamic sky conditions typical of temperate climates, with high-resolution, minute-by-minute irradiance measurements. A major focus of this work is the evaluation of feasibility software limitations, specifically in its ability to estimate Diffuse Horizontal Irradiance (DHI) and Direct Normal Irradiance (DNI) from Global Horizontal Irradiance (GHI) using decomposition models, as well as its transposition models for calculating Plane of Array (POA) irradiance. The contributions of this research are fourfold: firstly, this thesis develops a robust benchmarking framework to evaluate decomposition models using tests for temporal resolution, spatial homogeneity, and the influence of dataset periods. Since many locations lack dedicated weather stations for DHI and DNI measurements, such as the case study in the UK, this framework becomes essential. Initially, 5 decomposition models were identified as robust. To broaden the range, this research introduces the effect of clear-sky GHI (GHIClear) by altering 10 variations, expanding the pool of viable models from 5 to 15. Secondly, through the separation of sky conditions into clear, intermediate, and overcast days, the study evaluates the performance of transposition models within feasibility software. The DISC decomposition model, when paired with the Skartveit-Olseth (SO) transposition model, demonstrated consistent performance for both a fixed-tilt (FT) system at 55 degrees and a tracking system. Additional testing, using six distinct cloud intervals and feeding in measured GHI, DHI, and DHI, further confirmed the robustness of the SO model. Thirdly, work in this thesis assesses the reliability of six clear-sky irradiance model iterations using data from 67 global stations and different data sources, including measured values from AErosol RObotic NETwork (AERONET), Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), and Copernicus Atmosphere Monitoring Service (CAMS). The analysis focuses on key atmospheric parameters such as precipitable water (PW), the Ångström exponent (AE), and Aerosol Optical Depth at 550 nm (AOD550). MERRA-2 data outperformed CAMS in all 3 parameter estimations and was second only to AERONET’s measured data when coupled with the REST2 proprietary model. Among openaccess model services, McClear proved superior to ERA5, making it the most reliable option. 5 Lastly, with the growing adoption of AgriPV systems, there is an increasing demand for accurate estimation of Photosynthetically Active Radiation (PAR) irradiance, a critical aspect currently underrepresented in existing feasibility software for AgriPV applications. A new model, the Musleh-Rahman (MR) model, is introduced to accurately predict PAR in temperate climates using easily accessible input parameters. This model is designed to enhance the accuracy of PAR estimations and was benchmarked against 10 other PAR models, outperforming them all. The combined findings of this research provide a foundation for enhancing sub-hourly irradiance prediction accuracy. These insights are expected to support the PV industry’s expansion into new regions and facilitate the seamless integration of advanced PV technologies into feasibility software, ultimately driving the growth of renewable energy deployment.
University of Southampton
Musleh, Yazan J.K.
09defaa3-c8de-408e-884d-722f01b3843f
Musleh, Yazan J.K.
09defaa3-c8de-408e-884d-722f01b3843f
Boden, Stuart
83976b65-e90f-42d1-9a01-fe9cfc571bf8
Rahman, Tasmiat
e7432efa-2683-484d-9ec6-2f9c568d30cd

Musleh, Yazan J.K. (2025) Development and validation of optical models for predicting solar irradiance in temperate climates. University of Southampton, Doctoral Thesis, 246pp.

Record type: Thesis (Doctoral)

Abstract

As the global energy landscape shifts toward renewable sources, particularly photovoltaic (PV) technology, PV installations are expanding rapidly, covering larger areas and integrating advanced innovations. This growth poses new challenges for feasibility software used to evaluate solar projects, especially as diverse regions adopt emerging PV technologies. Accurate solar irradiance prediction plays a crucial role in ensuring the reliability of feasibility assessments and financial forecasting, as these predictions form the backbone of feasibility software's performance. This research delves into advanced PV technologies, including bifacial panels, tracking systems, and AgriPhotovoltaics (AgriPV), focusing on their behavior under the dynamic sky conditions typical of temperate climates, with high-resolution, minute-by-minute irradiance measurements. A major focus of this work is the evaluation of feasibility software limitations, specifically in its ability to estimate Diffuse Horizontal Irradiance (DHI) and Direct Normal Irradiance (DNI) from Global Horizontal Irradiance (GHI) using decomposition models, as well as its transposition models for calculating Plane of Array (POA) irradiance. The contributions of this research are fourfold: firstly, this thesis develops a robust benchmarking framework to evaluate decomposition models using tests for temporal resolution, spatial homogeneity, and the influence of dataset periods. Since many locations lack dedicated weather stations for DHI and DNI measurements, such as the case study in the UK, this framework becomes essential. Initially, 5 decomposition models were identified as robust. To broaden the range, this research introduces the effect of clear-sky GHI (GHIClear) by altering 10 variations, expanding the pool of viable models from 5 to 15. Secondly, through the separation of sky conditions into clear, intermediate, and overcast days, the study evaluates the performance of transposition models within feasibility software. The DISC decomposition model, when paired with the Skartveit-Olseth (SO) transposition model, demonstrated consistent performance for both a fixed-tilt (FT) system at 55 degrees and a tracking system. Additional testing, using six distinct cloud intervals and feeding in measured GHI, DHI, and DHI, further confirmed the robustness of the SO model. Thirdly, work in this thesis assesses the reliability of six clear-sky irradiance model iterations using data from 67 global stations and different data sources, including measured values from AErosol RObotic NETwork (AERONET), Modern-Era Retrospective analysis for Research and Applications, Version 2 (MERRA2), and Copernicus Atmosphere Monitoring Service (CAMS). The analysis focuses on key atmospheric parameters such as precipitable water (PW), the Ångström exponent (AE), and Aerosol Optical Depth at 550 nm (AOD550). MERRA-2 data outperformed CAMS in all 3 parameter estimations and was second only to AERONET’s measured data when coupled with the REST2 proprietary model. Among openaccess model services, McClear proved superior to ERA5, making it the most reliable option. 5 Lastly, with the growing adoption of AgriPV systems, there is an increasing demand for accurate estimation of Photosynthetically Active Radiation (PAR) irradiance, a critical aspect currently underrepresented in existing feasibility software for AgriPV applications. A new model, the Musleh-Rahman (MR) model, is introduced to accurately predict PAR in temperate climates using easily accessible input parameters. This model is designed to enhance the accuracy of PAR estimations and was benchmarked against 10 other PAR models, outperforming them all. The combined findings of this research provide a foundation for enhancing sub-hourly irradiance prediction accuracy. These insights are expected to support the PV industry’s expansion into new regions and facilitate the seamless integration of advanced PV technologies into feasibility software, ultimately driving the growth of renewable energy deployment.

Text
Final Thesis - Version of Record
Available under License University of Southampton Thesis Licence.
Download (11MB)
Text
Final-thesis-submission-Examination-Mr-Yazan-Musleh
Restricted to Repository staff only

More information

Published date: 13 May 2025

Identifiers

Local EPrints ID: 500919
URI: http://eprints.soton.ac.uk/id/eprint/500919
PURE UUID: 02298fa7-b8a8-42b2-ac89-a84b896d787c
ORCID for Yazan J.K. Musleh: ORCID iD orcid.org/0000-0001-9313-2528
ORCID for Stuart Boden: ORCID iD orcid.org/0000-0002-4232-1828
ORCID for Tasmiat Rahman: ORCID iD orcid.org/0000-0002-6485-2128

Catalogue record

Date deposited: 16 May 2025 16:40
Last modified: 11 Sep 2025 03:24

Export record

Contributors

Author: Yazan J.K. Musleh ORCID iD
Thesis advisor: Stuart Boden ORCID iD
Thesis advisor: Tasmiat Rahman ORCID iD

Download statistics

Downloads from ePrints over the past year. Other digital versions may also be available to download e.g. from the publisher's website.

View more statistics

Atom RSS 1.0 RSS 2.0

Contact ePrints Soton: eprints@soton.ac.uk

ePrints Soton supports OAI 2.0 with a base URL of http://eprints.soton.ac.uk/cgi/oai2

This repository has been built using EPrints software, developed at the University of Southampton, but available to everyone to use.

We use cookies to ensure that we give you the best experience on our website. If you continue without changing your settings, we will assume that you are happy to receive cookies on the University of Southampton website.

×