Daily guide · GB · 2026-08-27
The number your Great Britain 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 benchmark your model has to beat this week is a persistence mean absolute error of 24,06 EUR/MWh across the six days on record. That is the naive yesterday-equals-today baseline, and it sets the bar any serious forecast should clear comfortably.
What stands out is how uneven the week is. Persistence works well on 21 August, where the error is just 9,95 EUR/MWh against a day mean of 137 EUR/MWh, and again on 25 August at 14,27 EUR/MWh. It falls apart on 22 August, where the error reaches 49,24 EUR/MWh while the day mean drops to 89,65 EUR/MWh, the lowest of the week. That single day does most of the damage to the weekly average, with 24 August adding a further 32,57 EUR/MWh.
The takeaway is that the weekly headline of 24,06 EUR/MWh is not a smooth target but a blend of easy and hard days. If your model cannot outperform persistence around the 22 August dip, it will struggle to beat the week regardless of how it handles the calmer, higher-priced sessions near 137 EUR/MWh.
Before you train anything on GB 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:
24.06 €/MWh
persistence MAE on this week's GB auctions — the bar. A model that can't get under this adds negative value.
Why the bar moves: this week, day by day
| Target day | Day mean €/MWh | Persistence MAE |
|---|---|---|
| 2026-08-21 | 137 | 9.95 |
| 2026-08-22 | 89.65 | 49.24 |
| 2026-08-23 | 110.51 | 20.85 |
| 2026-08-24 | 137.16 | 32.57 |
| 2026-08-25 | 134.18 | 14.27 |
| 2026-08-26 | 147.4 | 17.49 |
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/GB?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/GB
(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-gb-2026-08-27".
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