arXiv (All)
open access
XWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms
AI power demand is growing at an unprecedented rate while power grids are often ailing and struggle to keep up. Grid expansion comes with high capital expenditure and long-distance transmission losses, yet there is abundant renewable energy at the source, just not matched to demand. This paper pro
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
- XWind: A Cross-site Router for Large Language Model Inference Serving at Renewable Energy Farms#222568
- Hybrid Semantic Context-Enhanced Ensemble Learning for Wind Power Ramp-Event Forecasting and Uncertainty-Aware Evaluation#793248
- Hybrid Semantic Context-Enhanced Ensemble Learning for Wind Power Ramp-Event Forecasting and Uncertainty-Aware Evaluation#789244
- 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
- A Kyiv Private Household Smart Plug Dataset for AI-Driven Digital Twin Modeling of Sustainable Residential Energy Systems#1060898
Record · ID 1117433
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