Probabilistic DAM price forecasting using a combined Quantile Regression Deep Neural Network with less-crossing quantiles

Conference paper (2021)

Authors

T.J.T. van der Heijden Water Resources - CEG
P. Palensky Intelligent Electrical Power Grids - EEMCS
E. Abraham Water Resources - CEG
Research Group
Water Resources (CEG) (TU Delft)
Copyright: © 2021 T.J.T. van der Heijden, P. Palensky, E. Abraham
DOI: https://doi.org/10.1109/IECON48115.2021.9589097
Quantile Regression Deep Neural Network Electricity Price Forecasting Day Ahead Market Crossing Quantile Problem
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Published Date

2021

Language

English

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Faculty

Civil Engineering & Geosciences

Department

Water Management

Research Group

Water Resources

Abstract

In this paper we propose a Quantile Regression Deep Neural Network capable of forecasting multiple quantiles in one model using a combined quantile loss function, and apply it to probabilistically forecast the prices of 8 European Day Ahead Markets. We show that the proposed loss function significantly reduces the quantile crossing problem to (near) 0% in all markets considered, while in some cases simultaneously increasing forecasting performance based on classical point forecast metrics applied to the expected value of the probabilistic forecast. The models are optimized using an automated approach with an elaborate feature- and hyperparameter search space, leading to good model performance in all considered markets.