Ensemble Complexity in Photovoltaic Forecasting
cs.LG, cs.AI
Submitted: 2026-09-14
Updated: 2026-09-14
License: http://creativecommons.org/licenses/by-sa/4.0/
The gist: An ensemble can improve photovoltaic forecasts while adding components that contribute little or increase computation.
Terminology
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.
Sources
- TSMixer: An All-MLP Architecture for Time Series Forecasting
- Mamba: Linear-Time Sequence Modeling with Selective State Spaces
- A Time Series is Worth 64 Words: Long-term Forecasting with Transformers
- TimeMixer: Decomposable Multiscale Mixing for Time Series Forecasting
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