Foundation-Style Pretraining for Scalable and Transferable Distributed PV Forecasting

Key points

• The rapid growth of distributed photovoltaic (PV) generation is increasing the demand for accurate forecasting across large numbers of geographically dispersed stations.1
• NWP-assisted forecasting is formulated as a sequence-completion problem that unifies historical PV power, meteorological observations, future numerical weather prediction (NWP) forecasts, and time encodings.1
• The mixed objective combines the operational structure of PV forecasting with random sequence reconstruction and provides the most balanced performance across forecasting accuracy, degraded NWP conditions, and downstream adaptation.1
• The pretrained model can also be directly deployed at new PV stations and subsequently adapted to region-specific operational objectives when local data become available, providing a scalable forecasting paradigm for large distributed PV fleets.1

Sources (1)

• [1]Foundation-Style Pretraining for Scalable and Transferable Distributed PV Forecasting
  arXiv (AI, ML, NLP, CV, robotics, multi-agent) · Oct 20, 07:26 AM

  To address this scalability gap, this paper presents a domain-specific foundation-style pretraining framework that learns a shared forecasting model from heterogeneous multi-site and multi-horizon PV data.

  The rapid growth of distributed photovoltaic (PV) generation is increasing the demand for accurate forecasting across large numbers of geographically dispersed stations.

Extractive summary: sentences quoted from the sources.