Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/137945
Full metadata record
DC FieldValueLanguage
dc.contributor.authorGoh, Wen Longen_US
dc.date.accessioned2020-04-20T05:55:09Z-
dc.date.available2020-04-20T05:55:09Z-
dc.date.issued2020-
dc.identifier.urihttps://hdl.handle.net/10356/137945-
dc.description.abstractTraditional grids have been used to distributed electricity to the consumers, however the electric supply is steadily becoming unstable as the electricity demand increases. Hence, this issue brings the need of a smart grid due to its ability to tackle this issue. The role of the smart grid is to be able to shift electricity consumption with various methods implemented by the agent. This paper will discuss on the role of the agent in the context of the smart grid, how the agent uses reinforcement learning for decision making, and how reinforcement learning is implemented.en_US
dc.language.isoenen_US
dc.publisherNanyang Technological Universityen_US
dc.relationSCSE19-0518en_US
dc.subjectEngineering::Computer science and engineeringen_US
dc.titleBuilding software agents for power trading agent competitionen_US
dc.typeFinal Year Project (FYP)en_US
dc.contributor.supervisorBo Anen_US
dc.contributor.schoolSchool of Computer Science and Engineeringen_US
dc.description.degreeBachelor of Engineering (Computer Science)en_US
dc.contributor.supervisoremailboan@ntu.edu.sgen_US
item.grantfulltextrestricted-
item.fulltextWith Fulltext-
Appears in Collections:SCSE Student Reports (FYP/IA/PA/PI)
Files in This Item:
File Description SizeFormat 
Final Year Report_GohWenLong.pdf
  Restricted Access
1.24 MBAdobe PDFView/Open

Page view(s)

274
Updated on Apr 27, 2025

Download(s)

14
Updated on Apr 27, 2025

Google ScholarTM

Check

Items in DR-NTU are protected by copyright, with all rights reserved, unless otherwise indicated.