Stage-oe-small.jpg

Article3268

Aus Aifbportal
Version vom 29. Juni 2022, 09:06 Uhr von Kf3052 (Diskussion | Beiträge)
(Unterschied) ← Nächstältere Version | Aktuelle Version (Unterschied) | Nächstjüngere Version → (Unterschied)
Wechseln zu:Navigation, Suche


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


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



Veröffentlicht: 2021 November

Journal: ACM SIGENERGY Energy Informatics Review
Nummer: 1
Seiten: 107-120
Verlag: ACM
Volume: 1


Referierte Veröffentlichung

BibTeX




Kurzfassung
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.

DOI Link: 10.1145/3508467.3508477



Forschungsgruppe

Effiziente Algorithmen


Forschungsgebiet

Energieinformatik