Publication2026
Multi-Agent Reinforcement Learning-based Optimization Framework for Smart Energy and Resource Management in Cyber-Physical Smart Grid Systems
The record
- Type
- Publication
- Date
- 1 January 2026
- Level
- National
- Journal
- 7th International Conference on Inventive Research in Computing Applications Icirca 2026 Proceedings
- Pages
- 721-726
- Published
- 1 January 2026
- DOI
- 10.1109/icirca69024.2026.11570622
- Scopus ID
- 105044143568
- Authors
- 6
- Open access
- No
- Citations
- 0
- International co-authorship
- No
- Co-author countries
- IN
Classification
Every label below carries the reasoning it was assigned on, the confidence band it was assigned in, and what produced it. The rubric is published.
UN Sustainable Development Goals
7 Ensure access to affordable, reliable, sustainable and modern energy for all
Hierarchical federated multi-agent reinforcement learning optimizes smart grid energy dispatch and renewable energy integration with 86.5% absorption improvement.
- High confidence
- The subject matter is a strong and direct inference from the record type, the funding or organising body, or the programme context, even where the title alone is thin. Example: an ICMR-funded project against SDG 3, on the strength of the agency.
- Judged
- Judged against the published classification rubric, under the rubric and prompt versions recorded with the run that produced it.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
9.5 Enhance scientific research, upgrade technological capabilities, and increase researchers and R&D spending
Advanced AI methodology for distributed grid optimization represents research innovation in energy management systems.
- Moderate confidence
- The alignment is plausible and defensible but rests on an assumption a reasonable reviewer could decline. Published, and surfaced in the review queue for a human to confirm or override.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Viksit Bharat @2047
vb-2.5 Green economy, energy transition and climate resilience
Smart grid optimization for renewable energy integration and operational cost reduction directly supports green economy transition.
- Produced by
- claude-haiku-4-5-20251001, under kortex-publication-v1
Each label above carries the instrument and the version that produced it. How each classification is reached.
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- Source rows resolved into this record
- 1
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Citing this record
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KIIT Deemed to be University. “Multi-Agent Reinforcement Learning-based Optimization Framework for Smart Energy and Resource Management in Cyber-Physical Smart Grid Systems”. Kortex.