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

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.

References in corpus (3)

• A tutorial on conformal prediction
• Proceedings of the Fourteenth Conference on Uncertainty in Artificial Intelligence (1998)
• Day-ahead electricity price forecasting with high-dimensional structures: Univariate vs. multivariate modeling frameworks

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• Conformal Prediction Bands for Two-Dimensional Functional Time Series
• Improved conformalized quantile regression
• Multivariate Simulation-based Forecasting for Intraday Power Markets: Modelling Cross-Product Price Effects
• Probabilistic forecasting with a hybrid Factor-QRA approach: Application to electricity trading
• Smoothing Quantile Regression Averaging: A new approach to probabilistic forecasting of electricity prices
• ReModels: Quantile Regression Averaging models
• Variance Stabilizing Transformations for Electricity Price Forecasting in Periods of Increased Volatility