# Machine learning-based probabilistic forecasting of solar irradiance in Chile

arXiv: https://arxiv.org/abs/2411.11073 · HTML: https://mlsanity.com/paper/2411.11073/

## Key facts

- Cited by 1 papers in the map (data through 14 Oct 2026).
- Builds on 9 of its 75 references that are in the map.
- First posted on arXiv 17 Nov 2024.

## Builds on (most cited first, 9 of 9)

- [Neural networks for post-processing ensemble weather forecasts](https://mlsanity.com/paper/1805.09091/) (2018)
- [Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison](https://mlsanity.com/paper/2106.09512/) (2021)
- [Generative machine learning methods for multivariate ensemble post-processing](https://mlsanity.com/paper/2211.01345/) (2022)
- [Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling](https://mlsanity.com/paper/1302.7149/) (2013)
- [Censored and shifted gamma distribution based EMOS model for probabilistic quantitative precipitation forecasting](https://mlsanity.com/paper/1512.04068/) (2015)
- [A two-step machine learning approach to statistical post-processing of weather forecasts for power generation](https://mlsanity.com/paper/2207.07589/) (2022)
- [Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation](https://mlsanity.com/paper/1904.07192/) (2019)
- [Clustering-based spatial interpolation of parametric post-processing models](https://mlsanity.com/paper/2401.14393/) (2024)
- [Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning](https://mlsanity.com/paper/2406.04424/) (2024)

## Built on by (most cited first, 1 of 1)

- [A Composite-Loss Graph Neural Network for the Multivariate Post-Processing of Ensemble Weather Forecasts](https://mlsanity.com/paper/2509.02784/) (2025)

## Abstract (arXiv)

By the end of 2023, renewable sources cover 63.4% of the total electric power demand of Chile, and in line with the global trend, photovoltaic (PV) power shows the most dynamic increase. Although Chile's Atacama Desert is considered the sunniest place on Earth, PV power production, even in this area, can be highly volatile. Successful integration of PV energy into the country's power grid requires accurate short-term PV power forecasts, which can be obtained from predictions of solar irradiance and related weather quantities. Nowadays, in weather forecasting, the state-of-the-art approach is the use of ensemble forecasts based on multiple runs of numerical weather prediction models. However, ensemble forecasts still tend to be uncalibrated or biased, thus requiring some form of post-processing. The present work investigates probabilistic forecasts of solar irradiance for Regions III and IV in Chile. For this reason, 8-member short-term ensemble forecasts of solar irradiance for calendar year 2021 are generated using the Weather Research and Forecasting (WRF) model, which are then calibrated using the benchmark ensemble model output statistics (EMOS) method based on a censored Gaussian law, and its machine learning-based distributional regression network (DRN) counterpart. Furthermore, we also propose a neural network-based post-processing method resulting in improved 8-member ensemble predictions. All forecasts are evaluated against station observations for 30 locations, and the skill of post-processed predictions is compared to the raw WRF ensemble. Our case study confirms that all studied post-processing methods substantially improve both the calibration of probabilistic- and the accuracy of point forecasts. Among the methods tested, the corrected ensemble exhibits the best overall performance. Additionally, the DRN model generally outperforms the corresponding EMOS approach.