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arXiv Machine Learning By RR Flanagan, E Kasha, PW Talbot, AS Epiny, MR Deinert

Two-stage residual learning for calibrated uncertainty in solar irradiance forecasts

Read the original on arXiv Machine Learning →

The paper presents a two‑stage residual learning approach for solar irradiance forecasting that separately predicts the expected irradiance with an LSTM and models the forecast error distribution with a second network. This method improves uncertainty estimation, reducing the deviation between predicted and historical standard deviations by 34.6 % for a standalone LSTM and 56.5 % when combined with Gaussian process regression. The approach captures total forecast error, providing more reliable uncertainty estimates for decision‑making.

Machine-generated by The Flow from the publisher's headline and feed description — not written or checked by a human. The full article lives at arXiv Machine Learning.

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