WF-13 · Italy Variable Renewable Forecast

Italy Variable Renewable Forecast

Forecasts the Europe/Rome D+1 civil day for solar, wind and total VRE, normalising by capacity and archiving point-in-time vintages.

Experimental · forecast · point-in-timeDaily 08:50 Europe/RomeEnergy · renewables · forecasting
Demonstration card: workflow summary, not evidence from a real plant.
1 · In one sentence

Forecasts the Europe/Rome D+1 civil day for solar, wind and total VRE, normalising by capacity and archiving point-in-time vintages.

2 · Why it exists

Produce a falsifiable D+1 solar and wind forecast normalised by capacity while preserving the information vintage available at forecast time.

3 · What it produces

P10/P50/P90, D+1 GWh, abundance index and OOS metrics versus baselines.

The images come from demonstration runs included in the attached repository and are shown without dates to illustrate workflow structure, method and evidence.

Workflow overview and primary output.
Calculation method and pipeline.
Evidence, metrics or checks from the demonstration run.
History, sensitivities or validation context.

4 · How it works

From data to output, with explicit controls.

1Observed generation/capacity when authorised
2ECMWF weather and temporal features
3Capacity-factor normalisation
4D+1 Gradient Boosting
5Intervals and baselines
6Vintage and OOS maturation

Transferable pattern: Capacity-normalised point-in-time forecasting.

5 · Data, AI/ML/RPA and method

Data and features

Terna Public API primary and ENTSO-E fallback for observed generation/capacity; without credentials, a non-publishable Open-Meteo proxy using declared 100 m wind nodes, local power curves before weighted aggregation, ECMWF D+1; 1/24/168h lags, seasonality and clear-sky

Calculation / inference

Gradient Boosting on capacity factor; empirical intervals

Validation

time split and persistence; proxy separated from actuals and never matured as a real forecast; prospective ENTSO-E A69 baseline when available; nMAE/RMSE/bias/coverage/skill

Stack

Python, pandas, scikit-learn, requests, SQLite, Matplotlib

6 · Technical dossier

Operating conditions and declared limitations.

Compute cadenceDaily 08:50 Europe/Rome
Planned publicationIT Tue 11:15 + Sun 11:30; EN Tue 17:00 + Sun 19:00
Possible applicationsload shifting, energy planning, storage and flexibility
Research usevintage bias and skill versus physical/operator baselines
Required conditionV1 covers solar+wind only and does not certify RFNBO
Failure modemistaking the weather/capacity proxy for observed generation, missing forecast vintages or installed-capacity growth mistaken for skill
Interpretation boundary

Without an authenticated observed source, the workflow uses a weather/capacity proxy explicitly not publishable as real generation. It does not certify RFNBO or site-level power availability.

7 · Verification and further reading

Read the workflow in the wider laboratory context.