Ensemble Complexity in Photovoltaic Forecasting
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The paper presents a hierarchical ensemble for short‑term photovoltaic power forecasting that fuses temporal neural models, historical analogs, state climatology, and gradient‑boosted trees with horizon‑specific convex weights. Solar geometry and numerical weather forecasts provide expected generation conditions, while a calibration step corrects recent bias using historical forecast errors. Evaluated on PVDAQ and GEFCom2014 data, the ensemble reduces mean absolute error by up to 6% compared to strong individual models, though its advantage varies with dataset and horizon.
The paper investigates whether combining a strong tree-based photovoltaic forecast model with other temporal predictors can lower its mean absolute error (MAE) over 15–240 minute horizons. A weighted ensemble, constrained to nonnegative weights that sum to one, is trained to minimize MAE, allowing unhelpful models to receive zero weight while the tree anchors the combination. On 56,078 windows from two co-located PVDAQ systems, the shared-MAE policy reduces scaled MAE by 3.21% relative to the tree alone, but adding the latest bounded neural correction does not improve performance further.
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