Synthetic demand data generation for individual electricity consumers:Inpainting  

作　　者：Dascha Dobrovolskij Hans-Georg Stark 

机构地区：[1]Universität Bremen,Bibliothekstraβe 5,28359 Bremen,Germany [2]Technische Hochschule Aschaffenburg,Würzburger Straβe45,63743 Aschaffenburg,Germany

出　　处：《Energy and AI》2024年第1期36-43,共8页能源与人工智能（英文）

基　　金：supported by the German Ministry of Education and Research(BMBF)within the project“AGENS:Analytischgenerative-Netzwerke zur Systemidentifikation”(grant no:05M20WFA).

摘　　要：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.

关 键 词：Time series Missing data points Time-frequency analysis Convex optimization Douglas-Rachford-algorithm Gabor transform 

分 类 号：TP39[自动化与计算机技术—计算机应用技术] TP31[自动化与计算机技术—计算机科学与技术]