Conformal Prediction Interval Estimations with an Application to Day-Ahead and Intraday Power Markets
This addresses the need for accurate uncertainty estimation in electricity markets, but it is incremental as it applies an existing method to a new domain.
The paper applied Conformal Prediction to short-term electricity price forecasting, demonstrating that it produces sharp and reliable prediction intervals in day-ahead and intraday power markets, with performance compared to state-of-the-art models like quantile regression averaging.
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.