Post-processing of ensemble photovoltaic power forecasts with distributional and quantile regression methods

2025/08/21 by Mayer, Martin János, Baran, Ágnes, Lerch, Sebastian +3
#Applications (stat.AP) #FOS: Computer and information sciences

paper · doi:10.48550/arxiv.2508.15508

Abstract

Accurate and reliable forecasting of photovoltaic (PV) power generation is crucial for grid operations, electricity markets, and energy planning, as solar systems now contribute a significant share of the electricity supply in many countries. PV power forecasts are often generated by converting forecasts of relevant weather variables to power predictions via a model chain. The use of ensemble simulations from numerical weather prediction models results in probabilistic PV forecasts in the form of a forecast ensemble. However, weather forecasts often exhibit systematic errors that propagate through the model chain, leading to biased and/or uncalibrated PV power predictions. These deficiencies can be mitigated by statistical post-processing. Using PV production data and corresponding short-term PV power ensemble forecasts at seven utility-scale PV plants in Hungary, we systematically evaluate and compare seven state-of-the-art methods for post-processing PV power forecasts. These include both parametric and non-parametric techniques, as well as statistical and machine learning-based approaches. Our results show that compared to the raw PV power ensemble, any form of statistical post-processing significantly improves the predictive performance. Non-parametric methods outperform parametric models, with advanced nonlinear quantile regression models showing the best results. Furthermore, machine learning-based approaches surpass their traditional statistical counterparts.

Citations

• AIFS-CRPS: Ensemble forecasting using a model trained with a loss function based on the Continuous Ranked Probability Score
• Probabilistic weather forecasting with machine learning
• Machine learning-based probabilistic forecasting of solar irradiance in Chile
• Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning
• AIFS -- ECMWF's data-driven forecasting system
• Uncertainty Quantification for Data-Driven Weather Models
• A Tutorial Review of the Solar Power Curve: Regressions, Model Chains, and Their Hybridization and Probabilistic Extensions
• Learning skillful medium-range global weather forecasting
• High-resolution (1 km) Köppen-Geiger maps for 1901–2099 based on constrained CMIP6 projections
• Evaluation of forecasts by a global data-driven weather model with and without probabilistic post-processing at Norwegian stations
• Accurate medium-range global weather forecasting with 3D neural networks
• Ensemble weather forecast post-processing with a flexible probabilistic neural network approach
• Aggregating distribution forecasts from deep ensembles
• A Concise Overview on Solar Resource Assessment and Forecasting
• Machine learning methods for postprocessing ensemble forecasts of wind gusts: A systematic comparison
• Optuna: A Next-generation Hyperparameter Optimization Framework
• Evaluating Probabilistic Forecasts with scoringRules
• “The Stippling Shows Statistically Significant Grid Points”: How Research Results are Routinely Overstated and Overinterpreted, and What to Do about It
• Predictive Inference Based on Markov Chain Monte Carlo Output
• Making and Evaluating Point Forecasts
• Strictly Proper Scoring Rules, Prediction, and Estimation
• Calculation of the PV modules angular losses under field conditions by means of an analytical model
• Comparing Predictive Accuracy
• The Stationary Bootstrap
• Improvement and validation of a model for photovoltaic array performance
• Solar position algorithm for solar radiation applications

Related

• Improving Model Chain Approaches for Probabilistic Solar Energy Forecasting through Post-processing and Machine Learning
• PV Power Forecasting Using Weighted Features for Enhanced Ensemble Method
• What drives the accuracy of PV output forecasts?
• Nearest-Neighbor Based Non-Parametric Probabilistic Forecasting with\n Applications in Photovoltaic Systems
• An Ensemble Framework For Day-Ahead Forecast of PV Output Power in Smart Grids