arXiv (All)
open access
Grounded, Compute-Efficient LLM Policy Agents for Energy-Poverty Equity in Physically-Constrained Peer-to-Peer Energy Markets
Energy poverty is nearly absent from NLP-for-social-good, and the little existing work is either static retrieval/QA or relies on carbon-intensive cloud LLMs, a self-defeating "computational irony" for a humanitarian setting. We present EqGrid, a closed-loop simulation in which a low-frequency, open
Conceptio Open Archive
Analyze & explore related research in Conceptio
Connect this record directly into your research workflow or AI agent. Explore citations across open repositories, query full text, and export structured proof bundles.
This document is indexed with metadata only: full text is not available in the archive for this record. Open the official source ↗
Related documents
- Hydropower-sited modular compute for AI: A Canada-first economic–ecological pathway#725677
- EcoKube: Simulating Carbon-Aware Scheduling Policies in Heterogeneous Edge-Cloud Environments#361427
- Quantifying AI impact in energy transitions: The Energy Justice Impact Assessment (EJIA) framework#1078081
- Smart Adaptive Computing Across the Continuum: LLMs in IoT-Edge-Cloud Resource Management#987105
- Greenpixie's AI Token Methodology: Assessing the Energy, Water and $\mathrm{CO_2\text{-}eq}$ Impact of AI Tokens for Open and Closed Weight Models#1119038
Record · ID 822336
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Retrieved via Conceptio: every document is proof-bundled with source, license, and retrieval metadata.
Bring this data into your agent: Connect via MCP or API · 20 free credits to start · no card required
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive
Read the Docs → · View CLI source on GitHub · Conceptio Open Knowledge Archive