SJSU ScholarWorks
metadata only
Cloud-Based AI Framework for EV Charging Forecasting and Infrastructure Optimization
The growing use of electric vehicles (EVs) has created a strong need for smart, data-driven charging management systems that can support large-scale and sustainable infrastructure. This study introduces a modular cloud-based framework that combines artificial intelligence and machine learning to 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
- AI for AI: Optimizing Additional Infrastructure Build-out to Power Artificial Intelligence Data Centers#985546
- AI-Powered Cloud Migration: Automating the Transition from On-Premises to Cloud Environments with Zero Downtime#81625
- A Review of Further Directions for Artificial Intelligence, Machine Learning, and Deep Learning in Smart Logistics#478122
- AI in Climate Change Mitigation#72860
- Artificial intelligence for clean technologies: a cross-sectoral review of sustainable, data-driven, and policy-aware systems#1068021
Record · ID 1081866
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