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Synthetic demand data generation for individual electricity consumers: Inpainting

  • In this contribution we deal with the problem of producing “reasonable” data, when considering recorded energy consumption data, which are at certain sections incomplete and/or erroneous. This task is important, when energy providers employ prediction models for expected energy consumption, which are based on past recorded consumption data, which then of course should be reliable and valid. In a related contribution Yilmaz (2022), GAN-based methods for producing such “artificial data” have been investigated. In this contribution, we describe an alternative and complementary method based on signal inpainting, which has been successfully applied to audio processing Lieb and Stark (2018). After giving a short overview of the theory of proximity-based convex optimization, we describe and adapt an iterative inpainting scheme to our problem. The usefulness of this approach is demonstrated by analyzing real-world-data provided by a German energy supplier.

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Metadaten
Author:Dascha Dobrovolskij, Hans-Georg Stark
Parent Title (English):Energy and AI
Document Type:Article
Language:English
Year of Completion:2024
Date of first Publication:2024/01/01
Release Date:2024/02/27
GND Keyword:Energieverbrauch; Energieversorgungsunternehmen
Volume:15
Issue:Januar 2024
First Page:100312
Last Page:100320
Urheberrecht:0
Institutes:Einrichtungen / Kompetenzzentrum Künstliche Intelligenz
research focus :Intelligent Systems / Artifical Intelligence and Data Science
Reviewed:ja
Licence (German):Creative Commons - CC0 1.0 - Universell - Public Domain Dedication
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