arXiv (All)
open access
Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model
Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load forecasting challenging. Recent advances in time-series foundation models (TSFMs) resulted in promising performance in zero-shot univariate load forecasting
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
- When, Not How Much: Evaluating Time-Series Foundation Models on Sparse Events#1144353
- Power Forecasting, Modelling and Optimization of Microgrid Solar Energy/Wind/Fuel Cell/Battery#436373
- Baseline-improved Economic Model Predictive Control for Optimal Microgrid Dispatch#986782
- Intelligent Dispatch and Control Strategies for Microgrids: Integrating Model Predictive Control, Metaheuristic Optimization, and Machine Learning#415136
- The Impact of AI on the U.S. Electrical Grid#201202
Record · ID 974760
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