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Short-Term Density Forecasting of Low-Voltage Load using Bernstein-Polynomial Normalizing Flows

  • The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level to increase efficiency and ensure reliable control. However, high fluctuations and increasing electrification cause huge forecast variability, not reflected in traditional point estimates. Probabilistic load forecasts take uncertainties into account and thus allow more informed decision-making for the planning and operation of low-carbon energy systems. We propose an approach for flexible conditional density forecasting of short-term load based on Bernstein polynomial normalizing flows, where a neural network controls the parameters of the flow. In an empirical study with 3639 smart meter customers, our density predictions for 24h-ahead load forecasting compare favorably against Gaussian and Gaussian mixture densities. Furthermore, they outperform a non-parametric approach based on the pinball loss, especially in low-data scenarios.

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Author:Marcel ArpogausORCiD, Marcus Voss, Beate SickGND, Mark Nigge-Uricher, Oliver DürrORCiDGND
Parent Title (English):IEEE Transactions on Smart Grid
Publisher:Institute of Electrical and Electronics Engineers (IEEE)
Document Type:Article
Year of Publication:2023
Release Date:2023/03/22
Tag:Normalizing Flows; Probabilistic Regression; Deep Learning; Probabilistic Load Forecasting; Low-Voltage
Edition:Early Access
Page Number:11
Volltextzugriff für Angehörige der Hochschule Konstanz via Datenbank IEEE Xplore möglich
Open Access?:Ja
Relevance:Peer reviewed Publikation in Master Journal List
Licence (German):License LogoUrheberrechtlich geschützt