Technology Category
Planning, Routing, and Scheduling: Optimization of Spatio-temporal SystemsMachine Learning: Calibration & Uncertainty QuantificationReasoning under Uncertainty: Relational Probabilistic Models
A sub-hourly spatio-temporal statistical model for solar irradiance in Ireland using open-source data
🤖 AI Summary
To address the scarcity of ground-based solar irradiance observations and the limited accuracy of existing reanalysis datasets (e.g., ERA5) under Ireland’s maritime climate, this paper develops the first high spatiotemporal-resolution Bayesian spatiotemporal statistical model tailored to the region, enabling hourly and 10-minute irradiance forecasting. The model integrates open-access ground measurements with reanalysis data and employs rigorous cross-validation and multi-source benchmarking to deliver full uncertainty quantification. Results show that hourly predictions significantly outperform ERA5 in accuracy; while 10-minute forecasts exhibit slightly higher RMSE, their uncertainty estimates are more reliable, yielding photovoltaic (PV) power simulations closely aligned with observed outputs from residential and industrial systems. The framework is scalable and amenable to real-time deployment, offering a novel tool for system planning and operation in power grids with high PV penetration.
Technology Category
Application Category
📝 Abstract
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.
Problem
Research questions and friction points this paper is trying to address.
Predicting solar irradiance in Ireland's variable climate with sparse ground measurements
Developing Bayesian spatio-temporal model for hourly and sub-hourly irradiance estimation
Providing superior accuracy over existing datasets with full uncertainty quantification
Innovation
Methods, ideas, or system contributions that make the work stand out.
Bayesian spatio-temporal modeling framework for solar irradiance
Hourly and sub-hourly resolution predictions across Ireland
Provides uncertainty quantification and real-time implementation capability
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