Enhancing the reliability of probabilistic PV power forecasts using conformal prediction

Abstract

The increasing integration of renewable energy, particularly solar photovoltaic (PV) power, presents challenges for power system operation. Accurate forecasts of renewable energy are both financially beneficial for electricity suppliers and necessary for grid operators to optimize operation and avoid grid imbalances. This paper proposes a forecasting framework to implement conformal prediction (CP) on top of point prediction models, which predict the PV power on a day-ahead basis, to quantify the uncertainty of those predictions. Simple and multiple linear regression, along with random forest regression, are used to construct the point predictions based on weather forecasts. Several variants of CP, including weighted CP, CP with k-nearest neighbors (KNN), CP with Mondrian binning, and conformal predictive systems, are built to transform the point predictions into rigorous uncertainty intervals or cumulative distribution functions to enhance reliability. The framework's performance is evaluated using large datasets of weather predictions and PV power output in the Netherlands. Results indicate that CP combined with KNN and/or Mondrian binning after a linear regressor outperforms the corresponding linear quantile regressor. CP with KNN and Mondrian binning after using random forest regression demonstrates the most accurate probabilistic PV power forecasts, improving the weighted interval score by 14% compared to multiple linear quantile regression.