Robustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically Interpretable Analysis
Engineering use of AI forecasting models requires not only high nominal accuracy but also predictable behavior under uncertain inputs. In photovoltaic (PV) forecasting, this requirement is especially challenging because numerical weather prediction (NWP) errors are temporally correlated, state dependent, and physically coupled across variables. Existing evaluations, however, often rely on perfect forecast assumptions or simplistic perturbations that do not reflect these characteristics. This study presents a physically constrained robustness evaluation framework based on simulation, using virt
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- PossiblePossibly related (embedding) · 49%NatLabRockies/sup3r →
- PossiblePossibly related (embedding) · 49%OpenSTEF/openstef →
- PossiblePossibly related (embedding) · 48%Quantifying drivers of photovoltaic power generation at Bhadla using explainable machine learning and causal discovery - Nature →
- LinkedLinked via arxiv author · 85%Dandan Chen →
“Robustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically I”
- LinkedLinked via arxiv author · 85%Dongyan Zhao →
“Robustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically I”
- LinkedLinked via arxiv author · 85%Xuepeng Chen →
“Robustness of Deep Learning Models for PV Power Forecasting under NWP Forecast Errors: A Spatiotemporal and Physically I”
- FuzzySimilar title/name (fuzzy) · 87%aymericdamien/TopDeepLearning →
“Fuzzy title match (0.94): “Robustness of Deep Learning Models for PV Power Forecasting ” ≈ “aymericdamien/TopDeepLearning””
- FuzzySimilar title/name (fuzzy) · 59%sktime/pytorch-forecasting →
“Fuzzy title match (0.73): “Robustness of Deep Learning Models for PV Power Forecasting ” ≈ “sktime/pytorch-forecasting””