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This study evaluates the trade-offs between forecast accuracy and computational cost when building heterogeneous ensembles for photovoltaic power prediction. The research demonstrates that static fusion methods can outperform boosting in specific datasets but highlights significant redundancy among model components, suggesting a need for stricter component-wise evaluation to optimize inference speed. These findings are relevant to AI practitioners optimizing machine learning pipelines for renewable energy applications where efficiency is critical.
Ensemble Complexity in Photovoltaic Forecasting
arXiv:2609.15049v1 Announce Type: cross Abstract: An ensemble can improve photovoltaic forecasts while adding components that contribute little or increase computation. We assess these effects through matched comparisons and ablations of a fixed heterogeneous predictor bank. Hourly experiments use GEFCom2014 and three additional public datasets, with chronological partitions and three seeds. Under retrospective ERA5 assistance, static fusion reduces scaled mean absolute error against matched boosting by 1.11%, 4.41%, and 1.63% on PVDAQ, OPSD, and Ausgrid; only OPSD remains supported after multiple-comparison correction. Weather gating offers no consistent incremental benefit. Exploratory member removals show group-level dependence alongside individual redundancy. A separate, previously inspected fifteen-minute case replaces one neural member with a tree predictor: normalized error falls by 1.72%, but measured inference is slower. These findings support component-wise evaluation with explicit limits on weather availability and test-set reuse.
Abstract:An ensemble can improve photovoltaic forecasts while adding components that contribute little or increase computation. We assess these effects through matched comparisons and ablations of a fixed heterogeneous predictor bank. Hourly experiments use GEFCom2014 and three additional public datasets, with chronological partitions and three seeds. Under retrospective ERA5 assistance, static fusion reduces scaled mean absolute error against matched boosting by 1.11%, 4.41%, and 1.63% on PVDAQ, OPSD, and Ausgrid; only OPSD remains supported after multiple-comparison correction. Weather gating offers no consistent incremental benefit. Exploratory member removals show group-level dependence alongside individual redundancy. A separate, previously inspected fifteen-minute case replaces one neural member with a tree predictor: normalized error falls by 1.72%, but measured inference is slower. These findings support component-wise evaluation with explicit limits on weather availability and test-set reuse.
Machine Learning (cs.LG); Artificial Intelligence (cs.AI)
arXiv:2609.15049 [cs.LG]
(or arXiv:2609.15049v1 [cs.LG] for this version)
https://doi.org/10.48550/arXiv.2609.15049 Focus to learn more arXiv-issued DOI via DataCite (pending registration)
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