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

2025-08-21

AI Summary

To address systematic bias and uncertainty-induced miscalibration in photovoltaic (PV) power forecasting, this study systematically evaluates seven statistical post-processing methods for calibrating ensemble weather-driven PV forecasts, using operational data from seven large-scale Hungarian PV plants. The methods encompass distributional regression, quantile regression (both linear and nonlinear), parametric and nonparametric models, and diverse machine learning techniques. Results demonstrate that all post-processing approaches significantly outperform the raw ensemble forecasts. Notably, nonlinear quantile regression achieves the best overall performance—reducing the Continuous Ranked Probability Score (CRPS) by up to 18.3% while substantially improving probabilistic calibration, as evidenced by enhanced reliability diagrams and lower Brier scores. This confirms the superiority of nonparametric, nonlinear modeling for probabilistic calibration in PV forecasting.

Technology Category

• Machine Learning: Calibration & Uncertainty Quantification
• Reasoning under Uncertainty: Relational Probabilistic Models
• Intelligent Robots: State Estimation

Application Category

• Search and Retrieval-Augmented AI: Web learning to rank, online learning, and counterfactual learning for ranking
• User Modeling, Personalization and Recommendation: Fairness-aware retrieval and ranking
• Web Mining and Content Analysis: Robustness and generalizability of Web mining methods

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.

Problem

• Mitigating systematic errors in photovoltaic power forecasts
• Evaluating statistical post-processing methods for forecast calibration
• Comparing parametric and non-parametric regression techniques for PV prediction

Innovation

• Ensemble forecasts post-processed statistically
• Nonlinear quantile regression methods applied
• Machine learning outperforms traditional statistical approaches