arXiv Machine Learning
Foundation-Style Pretraining for Scalable and Transferable Distributed PV Forecasting
Read the original on arXiv Machine Learning →The Flow has not summarised this story yet — read it at arXiv Machine Learning.
Related stories
arXiv AI
Oct 15
Constructed regional photovoltaic generation fields improve ultra-short-term distributed solar forecasting
arXiv:2610.16110v1 Announce Type: new Abstract: Ultra-short-term forecasting of densely distributed photovoltaic (PV) systems remains challenging because external meteorological and satellite product...
arXiv AI
Jul 10
PARA-PV: Physics-Aware Retrieval-Augmented PV Prediction Based on Frozen Foundation Model and Distribution Shift Correction
arXiv:2607. 08079v1 Announce Type: new Abstract: Accurate photovoltaic (PV) power forecasting is essential for reliable grid dispatch and renewable energy integration, yet it remains challenging because PV generation is jointly shaped by weather variability, day-night transitions, regime-dependent dynamics, and strict physical constraints.
arXiv Machine Learning
Aug 4
An AI-Based Decision-Support Pipeline for Day-Ahead Photovoltaic Forecasting
arXiv:2608. 02088v1 Announce Type: new Abstract: Reliable photovoltaic (PV) forecasts are needed for low-carbon energy systems, but newly deployed sites often have short, imperfect records.
arXiv Machine Learning
Jun 8
Time series Foundation Models based on Physics-Informed Synthetic Histories for Cold-Start Photovoltaic Forecasting
arXiv:2606. 07457v1 Announce Type: new Abstract: At commissioning time, Photovoltaic (PV) operators must forecast production before target-site observations are available, limiting the direct use of standard supervised forecasters.
arXiv AI
Sep 15
Horizon-specific Expert Fusion for Photovoltaic Power Forecasting
The paper presents a hierarchical ensemble for short‑term photovoltaic power forecasting that fuses temporal neural models, historical analogs, state climatology, and gradient‑boosted trees with horizon‑specific convex weights. Solar geometry and numerical weather forecasts provide expected generation conditions, while a calibration step corrects recent bias using historical forecast errors. Evaluated on PVDAQ and GEFCom2014 data, the ensemble reduces mean absolute error by up to 6% compared to strong individual models, though its advantage varies with dataset and horizon.
arXiv Machine Learning
Jul 15
Robustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically Interpretable Analysis
arXiv:2607. 12954v1 Announce Type: cross Abstract: Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs.