Unified Fourier Graph-Based Spatiotemporal Learning and Corrected NWP for Multi-Site Ultra-Short Term Photovoltaic Power Forecasting
Bibliographic record
Abstract
Accurate multi-site ultra-short term photovoltaic (PV) power forecasting is essential for grid stability and efficient energy management. Existing methods are limited by coarse-resolution weather data and insufficient modeling of spatiotemporal dependencies. We propose a novel framework that combines bias-corrected high-resolution weather data with an Adaptive Fourier Graph Neural Network (PV-AFGNN) to capture complex spatial and temporal patterns. A Gated recurrent unit (GRU)-based encoder-decoder module refines Numerical Weather Prediction forecasts using local meteorological data, generating site-specific inputs for PV-AFGNN, which operates in the Fourier domain to model correlations efficiently. By jointly optimizing weather data correction and spatiotemporal learning, the framework achieves high accuracy even under challenging forecasting scenarios. Experiments on real-world multi-site PV datasets show that our method consistently outperforms state-of-the-art benchmarks, offering a scalable and robust solution for reliable energy scheduling, and enhanced integration of PV power into modern electricity systems.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.000 |
| Meta-epidemiology (narrow) | 0.001 | 0.001 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.001 | 0.002 |
| Science and technology studies | 0.002 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".