# Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets

## TL;DR

This paper introduces Conformal Prediction as a novel method for short-term electricity price forecasting, demonstrating its ability to produce reliable prediction intervals and comparing its performance with existing models.

## Contribution

It applies Conformal Prediction to electricity markets, showing its effectiveness and providing guidelines for optimal model configuration in this context.

## Key findings

- Conformal Prediction yields sharp, reliable prediction intervals.
- It outperforms or matches state-of-the-art models like QRA.
- The method is versatile across different market conditions.

## Abstract

We discuss a concept denoted as Conformal Prediction (CP) in this paper. While initially stemming from the world of machine learning, it was never applied or analyzed in the context of short-term electricity price forecasting. Therefore, we elaborate the aspects that render Conformal Prediction worthwhile to know and explain why its simple yet very efficient idea has worked in other fields of application and why its characteristics are promising for short-term power applications as well. We compare its performance with different state-of-the-art electricity price forecasting models such as quantile regression averaging (QRA) in an empirical out-of-sample study for three short-term electricity time series. We combine Conformal Prediction with various underlying point forecast models to demonstrate its versatility and behavior under changing conditions. Our findings suggest that Conformal Prediction yields sharp and reliable prediction intervals in short-term power markets. We further inspect the effect each of Conformal Prediction's model components has and provide a path-based guideline on how to find the best CP model for each market.