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

TL;DR

This paper evaluates various statistical post-processing methods to improve the accuracy and calibration of ensemble photovoltaic power forecasts, demonstrating that advanced nonlinear and machine learning techniques significantly outperform traditional models.

Contribution

It systematically compares seven state-of-the-art post-processing methods, highlighting the superior performance of non-parametric and machine learning approaches for PV forecast calibration.

Findings

Non-parametric methods outperform parametric models.

Machine learning approaches surpass traditional statistical methods.

Post-processing significantly improves forecast accuracy.

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.