Konformalna prognoza przedziałowa na potrzeby prognozowania cen energii 
elektrycznej na rynku dnia nastepnego i bieżącego  
[Conformal prediction interval estimations with an application to day-ahead and 
intraday power markets]

Christopher Kath (HEMF, Universität Duisburg-Essen, D)  


We discuss the concept of Conformal Prediction (CP). 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 special and explain why its simple yet very efficient 
idea has worked in other fields of applications. We compare its performance with 
different state-of-the-art 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 forecasting 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 electricity markets, better than other commonly used approaches. 
This performance is mostly due to the symmetric character of Conformal Prediction.