Daily guide · IT-SARD · 2026-09-05
The number your Italy Sardinia price model has to beat this week
All numbers computed from the week's published auction results at publication and frozen — this article is an honest snapshot, not a page that rewrites itself. Reproduce via the API.
The number to beat this week is 20,53 EUR/MWh: that is the mean absolute error a naive persistence forecast would have delivered across these five days in Italy Sardinia. Any model worth running should clear it comfortably, but the week is not uniform. Persistence errors range from 39,30 EUR/MWh on 31 August down to 10,38 EUR/MWh on 4 September, so the difficulty is heavily front-loaded.
That spread matters because it tracks the price regime. The early days sit lower and move more, with day means of 175,38 and 194,36 EUR/MWh and the largest persistence errors. By the end of the window prices settle near 210 to 212 EUR/MWh and persistence MAE falls below 12 EUR/MWh, meaning yesterday's price is already a strong guess. Your model's edge has to be earned at the start, not the finish.
The concrete takeaway: benchmark against 20,53 EUR/MWh for the week, but judge yourself day by day. Beating persistence on 3 and 4 September is easy; the real test is 31 August. For context, our own 30-day MAE is 20,94 against a 24,72 baseline.
Before you train anything on IT-SARD prices, you need the number your model has to beat. In day-ahead forecasting that number is naive persistence: predict that tomorrow's curve equals today's. It costs nothing, needs no features, and it is embarrassingly hard to beat — because power prices are dominated by daily and weekly rhythms that persistence gets for free. We computed it on this exact week:
20.53 €/MWh
persistence MAE on this week's IT-SARD auctions — the bar. A model that can't get under this adds negative value.
20.94 vs 24.72
our live production model vs its persistence baseline, trailing 345 scored days — the same scoring, in public, on /accuracy
Why the bar moves: this week, day by day
| Target day | Day mean €/MWh | Persistence MAE |
|---|---|---|
| 2026-08-30 | 175.38 | 24 |
| 2026-08-31 | 194.36 | 39.3 |
| 2026-09-02 | 211.09 | 17.64 |
| 2026-09-03 | 212.1 | 11.34 |
| 2026-09-04 | 210.67 | 10.38 |
Calm days make persistence look unbeatable; transition days (weather swings, weekend boundaries) are where a real model earns its keep. Averages hide this — always look at the daily distribution.
The part everyone gets wrong: the features
The model is the easy half. The silent killer is lookahead: training on weather reanalysis instead of the forecast that existed at issue time, on revised load data, on same-day flow averages that hadn't finished flowing at the auction. Backtests built that way look brilliant and die in production. Our Quant feature matrices serve one row per (issue day, 15-minute slot) where every column is documented with when it became knowable — the same rows our own models train on:
curl -H "Authorization: Bearer YOUR_KEY" \
"https://voltcast.com/api/v1/features/IT-SARD?issue_from=2026-07-01&horizons=1"
# columns include: price lags · previous-run weather (as forecast AT issue)
# · grid drivers (lagged to last COMPLETE day) · day-ahead load/RES forecasts
# · ensemble spread · target_price label (null until matured, never imputed)
Start with Home: the persistence bar above needs nothing but GET /v1/prices/IT-SARD
(Home covers one zone of your choice). When your walk-forward backtest beats it honestly, prove it in public on the
Forecast Bench — forward-blind, open scoring code, and our
own model is auto-entered under identical rules. If you'd rather buy the plumbing than build it,
contact us about ML-grade data access.
Method & citation. Prices are hourly means of published day-ahead auction results (native 15-minute periods averaged; ENTSO-E/SMARD, attributed). Wholesale-price component only — grid fees and taxes come on top and vary by supplier. Cite as "Voltcast Research, voltcast.com/guides/quant-features-it-sard-2026-09-05".
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