Konferenzbeitrag
Overview of machine learning and data-driven methods in agent-based modeling of energy markets
Lade...
Volltext URI
Dokumententyp
Text/Conference Paper
Dateien
Zusatzinformation
Datum
2019
Autor:innen
Zeitschriftentitel
ISSN der Zeitschrift
Bandtitel
Verlag
Gesellschaft für Informatik e.V.
Zusammenfassung
Local energy markets (LEM) allow prosumers and consumers to trade energy directly between each other and offer flexibility services to the grid. The benefits and challenges related to such markets need to be identified, and agent-based modeling (ABM) is a useful method to conduct simulation experiments with different market structures and clearing mechanisms. Machine learning (ML) and data-driven methods when integrated with ABM show great potential for constructing new distributed, agent-level knowledge. In this paper, we discuss the requirements for coupling ML methods and ABM. We also provide an overview of published literature on the common methods of integration of ML and data-driven methods in ABM of energy markets and discuss how these requirements are commonly addressed.