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Deep learning models show resilience to weather forecast errors in PV power prediction

By PulseAugur Editorial · [2 sources] ·

A new study evaluates the robustness of various deep learning models, including PatchTST, GRU, N-HITS, and LightGBM, when subjected to errors in numerical weather prediction (NWP) data. The research introduces a physically constrained framework to simulate these errors and assess their impact on photovoltaic (PV) power forecasting. Findings indicate that sequence models offer superior noise filtering and temporal resilience compared to tabular models, especially under significant disturbance levels. Techniques like SHAP and Integrated Gradients reveal that models can shift their predictive reliance from corrupted future forecasts to more stable historical data and physical priors. AI

IMPACT Enhances understanding of AI model reliability in real-world, uncertain conditions, guiding selection for critical forecasting tasks.

RANK_REASON The cluster contains an academic paper detailing a new evaluation framework and analysis of existing models.

Read on arXiv cs.LG →

AI-generated summary · Google Gemini · from 2 sources. How we write summaries →

Deep learning models show resilience to weather forecast errors in PV power prediction

COVERAGE [2]

  1. arXiv cs.LG TIER_1 English(EN) · Dandan Chen, Yan Zhao, Xuepeng Chen ·

    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. In photovoltaic (PV) forecasting, this requirement is especially challenging because numerical …

  2. arXiv cs.LG TIER_1 English(EN) · Xuepeng Chen ·

    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 cor…

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