Publication:

Control Policy Correction Framework for Reinforcement Learning-based Energy Arbitrage Strategies

Date

2024-01-01
Proceedings Paper
https://doi.org/10.1145/3632775.3661948
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dc.contributor.authorKarimi Madahi, Seyed Soroush
dc.contributor.authorGokhale, Gargya
dc.contributor.authorVerwee, Marie-Sophie
dc.contributor.authorClaessens, Bert
dc.contributor.authorDevelder, Chris
dc.date.accessioned2025-12-16T09:34:22Z
dc.date.available2025-12-16T09:34:22Z
dc.date.createdwos2025-09-25
dc.date.issued2024-01-01
dc.description.wosFundingTextWe would like to thank AlphaESS and iinno-benelux companies for providing the battery and their technical support. This research has received funding from the Energy Transition Fund (FOD Economy) via the FlexMyHeat project. Part of the research leading to these results has received funding from Agentschap Innoveren & Ondernemen (VLAIO) as part of the Strategic Basic Research (SBO) program under the InduFlexControl-2 project.
dc.identifier.doi10.1145/3632775.3661948
dc.identifier.urihttps://imec-publications.be/handle/20.500.12860/58582
dc.language.isoeng
dc.provenance.editstepusergreet.vanhoof@imec.be
dc.publisherASSOC COMPUTING MACHINERY
dc.source.beginpage123
dc.source.conference15TH ACM INTERNATIONAL CONFERENCE ON FUTURE AND SUSTAINABLE ENERGY SYSTEMS, E-ENERGY 2024
dc.source.conferencedate2024-06-04
dc.source.conferencelocationSingapore
dc.source.endpage133
dc.source.journalPROCEEDINGS OF THE 15TH ACM INTERNATIONAL CONFERENCE ON FUTURE AND SUSTAINABLE ENERGY SYSTEMS, E-ENERGY 2024
dc.source.numberofpages11
dc.title

Control Policy Correction Framework for Reinforcement Learning-based Energy Arbitrage Strategies

dc.typeProceedings paper
dspace.entity.typePublication
imec.internal.crawledAt2025-10-22
imec.internal.sourcecrawler
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