ZAPP: a zero-aware photovoltaic power prediction framework - Nature
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AbstractPredicting photovoltaic power generator outputs is not easy, especially when the values are near zero, because even very small errors can lead to large percentage errors. We introduce a new forecasting framework, ZAPP (Zero-Aware Photovoltaic Power Predictor), that handles near-zero errors while maintaining high prediction accuracy. The model does not rely on a single unified regression model but instead attaches two refinement stages to a base regression model. The first stage, called ZeroGate, predicts whether the output should be zero. The second refinement stage, the low-power MAPE reducer, fine-tunes the non-zero outputs near the zero level without significantly affecting scale-dependent regression metrics.
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