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

  • The transition to a fully renewable energy grid requires better forecasting of demand at the low-voltage level. However, high fluctuations and increasing electrification cause huge forecast errors with traditional point estimates. Probabilistic load forecasts take future uncertainties into account and thus enables various applications in 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 363 smart meter customers, our density predictions compare favorably against Gaussian and Gaussian mixture densities and also outperform a non-parametric approach based on the pinball loss for 24h-ahead load forecasting for two different neural network architectures.

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Metadaten
Author:Marcel ArpogausORCiD, Marcus VoßORCiD, Beate SickGND, Mark Nigge-Uricher, Oliver DürrORCiDGND
URN:urn:nbn:de:bsz:kon4-opus4-29255
URL:https://www.climatechange.ai/papers/icml2021/20/paper.pdf
Parent Title (English):ICML 2021, Workshop Tackling Climate Change with Machine Learning, June 26, 2021, virtual
Document Type:Conference Proceeding
Language:English
Year of Publication:2021
Release Date:2021/12/20
Tag:Power and energy; Deep Learning; Probabilistic forecasting; Generative modeling; Uncertainty quantification and robustness
Page Number:6
Article Number:#20
Institutes:Institut für Angewandte Forschung - IAF
Open Access?:Ja
Relevance:Peer reviewed nach anderen Listungen (mit Nachweis zum Peer Review Verfahren)
Licence (German):License LogoUrheberrechtlich geschützt