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| Main Authors: | , , , |
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| Format: | Preprint |
| Published: |
2025
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| Subjects: | |
| Online Access: | https://arxiv.org/abs/2509.21041 |
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| _version_ | 1866914056423604224 |
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| author | Upton, Maeve Organ, Eamonn Lenzi, Amanda Sweeney, James |
| author_facet | Upton, Maeve Organ, Eamonn Lenzi, Amanda Sweeney, James |
| contents | Accurate estimation of solar irradiance is essential for reliable modelling of solar photovoltaic (PV) power production. In Ireland's highly variable maritime climate, where ground-based measurement stations are sparsely distributed, selecting an appropriate solar irradiance dataset presents a significant challenge. This study introduces a novel Bayesian spatio-temporal modelling framework for predicting solar irradiance at hourly and sub-hourly (10-minute) resolutions across Ireland. Cross-validation demonstrates that our model is statistically robust across all temporal resolutions with hourly showing highest prediction precision whereas 10-minute resolution encounters higher errors but better uncertainty quantification. In separate evaluations, we compare our model against alternative data sources, including reanalysis datasets and nearest-station interpolation, and find that it consistently provides superior site-specific accuracy. At the hourly scale, our model outperforms ERA5 in agreement with ground-based observations. At the sub-hourly scale, 10-minute resolution estimates provide solar PV power outputs consistent with residential and industrial solar PV installations in Ireland. Beyond surpassing existing datasets, our model delivers full uncertainty quantification, scalability and the capacity for real-time implementation, offering a powerful tool for solar energy prediction and the estimation of losses due to overload clipping from inverter undersizing. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2509_21041 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | A sub-hourly spatio-temporal statistical model for solar irradiance in Ireland using open-source data Upton, Maeve Organ, Eamonn Lenzi, Amanda Sweeney, James Applications Accurate estimation of solar irradiance is essential for reliable modelling of solar photovoltaic (PV) power production. In Ireland's highly variable maritime climate, where ground-based measurement stations are sparsely distributed, selecting an appropriate solar irradiance dataset presents a significant challenge. This study introduces a novel Bayesian spatio-temporal modelling framework for predicting solar irradiance at hourly and sub-hourly (10-minute) resolutions across Ireland. Cross-validation demonstrates that our model is statistically robust across all temporal resolutions with hourly showing highest prediction precision whereas 10-minute resolution encounters higher errors but better uncertainty quantification. In separate evaluations, we compare our model against alternative data sources, including reanalysis datasets and nearest-station interpolation, and find that it consistently provides superior site-specific accuracy. At the hourly scale, our model outperforms ERA5 in agreement with ground-based observations. At the sub-hourly scale, 10-minute resolution estimates provide solar PV power outputs consistent with residential and industrial solar PV installations in Ireland. Beyond surpassing existing datasets, our model delivers full uncertainty quantification, scalability and the capacity for real-time implementation, offering a powerful tool for solar energy prediction and the estimation of losses due to overload clipping from inverter undersizing. |
| title | A sub-hourly spatio-temporal statistical model for solar irradiance in Ireland using open-source data |
| topic | Applications |
| url | https://arxiv.org/abs/2509.21041 |