Ensemble Complexity in Photovoltaic Forecasting
Quick summary
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 mult
Key takeaways
- 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.
Why it matters
The importance of “Ensemble Complexity in Photovoltaic Forecasting” will be measured by what changes in practice. User behavior, access conditions, verifiable performance and responsible-use outcomes are the signals worth following.
Member comments