Accurate photovoltaic (PV) power forecasting is essential for renewable energy integration, dynamic reserve allocation, and generation scheduling. Unpredicted generation ramps induce substantial voltage and frequency deviations on grid-connected distribution networks. This paper provides an objective benchmark among th...
Experimental results demonstrate that MSF-TransPV consistently outperforms persistence, statistical baselines, recurrent neural networks, and vanilla Transformer models in terms of RMSE, MAE, and normalized error metrics, while also providing reliable prediction intervals, indicating that explicit multi-source fusion a...
A hybrid PV forecasting framework that combines stacking ensemble learning with a targeted residual correction strategy, and demonstrates that analyzing error distribution and forecasting robustness provides valuable insights beyond conventional aggregate metrics, contributing to the development of more reliable photov...
Industrial microgrids integrating distributed photovoltaic (PV) systems require accurate grid exchange forecasting for effective energy management. However, grid exchange exhibits substantial fluctuations due to physical events such as cloud shading, PV curtailment, and surplus export. Most existing deep learning metho...
Accurate photovoltaic (PV) power forecasting is essential for enhancing grid stability, optimizing energy management, and facilitating the large-scale integration of renewable energy resources. Although deep learning techniques have demonstrated promising results in PV forecasting, their predictive performance is highl...
This study presents the adaptive three-expert ensemble (A3E), a reproducible framework for joint one-hour-ahead solar and wind power forecasting. A3E combines temporal, physically informed, and high-generation Extra Trees experts through a causal local-error gate and is evaluated under a strictly chronological, leakage...
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