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<p>Reliable short-term photovoltaic (PV) power forecasts are pivotal to grid balancing, intraday market clearing, and asset optimization. This study provides an in-depth comparative analysis of four state-of-the-art neural architectures–a convolutional-recurrent hybrid (CNN-LSTM), an LSTM-Autoencoder, a standalone LSTM, and a time-series Transformer–trained on three years of irradiance and power data with one-minute sampling interval from two meteorologically contrasting PV sites (semi-arid Colorado and desert Nevada). Each model is evaluated in a probabilistic forecasting setting, where kernel density estimation (KDE)-based residual post-processing and quantile extraction are used to obtain calibrated prediction intervals. The benchmark adopts an irradiance-driven forecasting framework, using historical irradiance observations as inputs and future PV power as the prediction target, thereby enabling a controlled assessment of each architecture’s ability to model the underlying irradiance-to-power relationship. Robustness is probed through three stressors: truncated training histories (1–3 years), temporal coarsening (1-, 5-, and 15-minute records), and up to 30% randomly or block-removed observations. Across all tests, CNN-LSTM consistently delivers the narrowest and most reliable intervals, leveraging its convolutional front-end to detect rapid cloud-edge ramps while the LSTM tail preserves long-range context. Compared with a strong LSTM benchmark, the hybrid reduces mean interval width by 22%, lowers the Continuous Ranked Probability Score (CRPS; a scoring rule measuring the distance between the forecast distribution and the observed outcome - lower is better) by 11%, and boosts empirical coverage by 5% (achieving 98.2% reliability). These advantages remain intact under data scarcity, coarse sampling, and substantial missingness, highlighting the critical role of architectures that unite local feature extraction with sequential memory. The findings confirm that high-fidelity point forecasts and rigorously quantified uncertainty can be achieved simultaneously, providing a clear path toward more dependable PV dispatch, reserve allocation, and market participation.</p>

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Short-term PV power forecasting under real-world data constraints: a benchmark study of neural networks with uncertainty quantification

  • 期刊论文

  • 出版模式：

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  • 发表日期：

    2026年8月4日

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  • Saloni Dhingra,
  • Giambattista Gruosso,
  • Giancarlo Storti Gajani

摘要

Reliable short-term photovoltaic (PV) power forecasts are pivotal to grid balancing, intraday market clearing, and asset optimization. This study provides an in-depth comparative analysis of four state-of-the-art neural architectures–a convolutional-recurrent hybrid (CNN-LSTM), an LSTM-Autoencoder, a standalone LSTM, and a time-series Transformer–trained on three years of irradiance and power data with one-minute sampling interval from two meteorologically contrasting PV sites (semi-arid Colorado and desert Nevada). Each model is evaluated in a probabilistic forecasting setting, where kernel density estimation (KDE)-based residual post-processing and quantile extraction are used to obtain calibrated prediction intervals. The benchmark adopts an irradiance-driven forecasting framework, using historical irradiance observations as inputs and future PV power as the prediction target, thereby enabling a controlled assessment of each architecture’s ability to model the underlying irradiance-to-power relationship. Robustness is probed through three stressors: truncated training histories (1–3 years), temporal coarsening (1-, 5-, and 15-minute records), and up to 30% randomly or block-removed observations. Across all tests, CNN-LSTM consistently delivers the narrowest and most reliable intervals, leveraging its convolutional front-end to detect rapid cloud-edge ramps while the LSTM tail preserves long-range context. Compared with a strong LSTM benchmark, the hybrid reduces mean interval width by 22%, lowers the Continuous Ranked Probability Score (CRPS; a scoring rule measuring the distance between the forecast distribution and the observed outcome - lower is better) by 11%, and boosts empirical coverage by 5% (achieving 98.2% reliability). These advantages remain intact under data scarcity, coarse sampling, and substantial missingness, highlighting the critical role of architectures that unite local feature extraction with sequential memory. The findings confirm that high-fidelity point forecasts and rigorously quantified uncertainty can be achieved simultaneously, providing a clear path toward more dependable PV dispatch, reserve allocation, and market participation.