Reinforcement learning based multi-agent system for smart microgrid
Authors:
- Niharika SinghNSNiharika Singh
- Kishu GuptaKGKishu Gupta
- Ashutosh Kumar SinghASAshutosh Kumar Singh
- Perumal NallagowndenPNPerumal Nallagownden
- Irraivan ElamvazuthiIEIrraivan Elamvazuthi
Open Access
- 1 October 2025
- journal article
- Published by Elsevier in Journal of Network and Computer Applications
- Vol. 244
Abstract
No abstract availableThis publication has 39 references indexed in Scilit:
- Reliability-assured service function chain migration strategy in edge networks using deep reinforcement learningJournal of Network and Computer Applications, 2024
- MATE: A multi-agent reinforcement learning approach for Traffic Engineering in Hybrid Software Defined NetworksJournal of Network and Computer Applications, 2024
- GROM: A generalized routing optimization method with graph neural network and deep reinforcement learningJournal of Network and Computer Applications, 2024
- FedMUP: Federated learning driven malicious user prediction model for secure data distribution in cloud environmentsApplied Soft Computing, 2024
- MAIDS: malicious agent identification-based data security model for cloud environmentsCluster Computing, 2024
- Optimal energy management strategies for energy Internet via deep reinforcement learning approachApplied Energy, 2019
- Energy 4.0: Towards IoT Applications in KazakhstanProcedia Computer Science, 2019
- Microgrids: A review of technologies, key drivers, and outstanding issuesRenewable and Sustainable Energy Reviews, 2018
- Multi-party energy management for smart building cluster with PV systems using automatic demand responseEnergy and Buildings, 2016
- Impact of solar and wind forecast uncertainties on demand response of isolated microgridsRenewable Energy, 2016