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

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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.
Original languageEnglish
Title of host publicationIECON 2021 – 47th Annual Conference of the IEEE Industrial Electronics Society
Subtitle of host publicationProceedings
PublisherIEEE
Number of pages6
ISBN (Electronic)978-1-6654-3554-3
ISBN (Print)978-1-6654-0256-9
DOIs
Publication statusPublished - 2021
EventIECON 2021: 47th Annual Conference of the IEEE Industrial Electronics Society - Virtual at Toronto, Canada
Duration: 13 Oct 202116 Oct 2021
Conference number: 47th

Conference

ConferenceIECON 2021
Country/TerritoryCanada
CityVirtual at Toronto
Period13/10/2116/10/21

Bibliographical note

Green Open Access added to TU Delft Institutional Repository 'You share, we take care!' - Taverne project https://www.openaccess.nl/en/you-share-we-take-care

Otherwise as indicated in the copyright section: the publisher is the copyright holder of this work and the author uses the Dutch legislation to make this work public.

Keywords

  • Quantile Regression
  • Electricity Price Forecasting
  • Deep Neural Network
  • Day Ahead Market
  • Crossing Quantile Problem

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