Machine learning-based probabilistic forecasting of solar irradiance in Chile

Abstract

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.

25 pages; 9 figures; 4 tables

References in corpus (15)

• Neural networks for post-processing ensemble weather forecasts
• Uncertainty Quantification in Complex Simulation Models Using Ensemble Copula Coupling
• Statistical Postprocessing for Weather Forecasts -- Review, Challenges and Avenues in a Big Data World
• Probabilistic Quantitative Precipitation Forecasting Using Ensemble Model Output Statistics
• Comparison of statistical post-processing methods for probabilistic NWP forecasts of solar radiation
• Censored and shifted gamma distribution based EMOS model for probabilistic quantitative precipitation forecasting
• Post-processing numerical weather prediction ensembles for probabilistic solar irradiance forecasting
• Statistical post-processing of wind speed forecasts using convolutional neural networks
• Similarity-based semi-local estimation of EMOS models
• Generative machine learning methods for multivariate ensemble post-processing
• Statistical post-processing of heat index ensemble forecasts: is there a royal road?
• A two-step machine learning approach to statistical post-processing of weather forecasts for power generation
• Statistical post-processing of ensemble forecasts of temperature in Santiago de Chile
• Parametric post-processing of dual-resolution precipitation forecasts
• Clustering-based spatial interpolation of parametric post-processing models

Cited by in corpus (2)

• A Composite-Loss Graph Neural Network for the Multivariate Post-Processing of Ensemble Weather Forecasts
• Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods