All news
Digital Twin

Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model

US & West ·

arXiv:2609.06656v1 Announce Type: cross Abstract: Modern power systems are growing increasingly complex as they integrate diverse generation sources to meet rising demand, making accurate load forecasting challenging.

Original headline
Assessing Covariate-Informed Grid Load Forecasting with a Time-Series Foundation Model
Read the original: arXiv — Systems and Control (eess.SY)

Translation and summary are machine-generated from the source. The full article is not republished; its rights belong to the source.

Related

EnergyUS & West

Initialization Is Key in Federated Short-Term Load Forecasting

The study focuses on federated learning for short-term load forecasting (STLF), addressing data privacy concerns. The authors identify structured heterogeneity in clients' load data: different responses to exogenous factors and distinct temporal load profiles, which degrade forecasting performance in federated learning. To mitigate these issues, they propose two model initialization strategies — global and local — that improve forecasting accuracy.

DT.ForecastarXiv — Systems and Control (eess.SY) ↗