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Automated generation of models for demand side flexibility using machine learning – an overview

Förderer, Kevin ORCID iD icon; Hagenmeyer, Veit; Schmeck, Hartmut


Flexibility in consumption and production provided by distributed energy resources (DERs) is a key to the integration of renewable energy sources into the energy system. However, even for identical DERs, the flexibility can vary widely, based on local constraints and circumstances. Therefore, handcrafting models can be labor-intensive and automating the generation of models could help increasing the volume of controllable flexibility in smart grids. Depending on the underlying mechanism for controlling demand side flexibility, there are various ways how an automation can be achieved. In this paper, we discuss fundamental concepts relevant to the automated generation of models for demand side flexibility, give an overview of different approaches, and point out fundamental differences. The main focus lies on model generation by means of machine learning techniques.

Verlagsausgabe §
DOI: 10.5445/IR/1000141687
Veröffentlicht am 10.01.2022
DOI: 10.1145/3508467.3508477
Zitationen: 2
Cover der Publikation
Zugehörige Institution(en) am KIT Institut für Angewandte Informatik und Formale Beschreibungsverfahren (AIFB)
Institut für Automation und angewandte Informatik (IAI)
Publikationstyp Zeitschriftenaufsatz
Publikationsmonat/-jahr 11.2021
Sprache Englisch
Identifikator ISSN: 2770-5331
KITopen-ID: 1000141687
HGF-Programm 37.12.03 (POF IV, LK 01) Smart Areas and Research Platforms
Erschienen in ACM SIGEnergy Energy Informatics Review
Band 1
Heft 1
Seiten 107–120
Projektinformation C/sells (BMWK, 03SIN123)
Schlagwörter Demand Side Management; MachineLearning; Automated Model Generation
Nachgewiesen in Dimensions
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