Forecast Accuracy Tracker
On July 24, 2026, the grid's own day-ahead demand forecast missed actual lower-48 demand by an average of 1.3% across the day's 24 hours, under-forecasting by 0.5% on balance. At the day's peak hour the miss was +0.8%. Over the past 12 months the typical daily miss has been 1.4%.
Daily forecast miss
Each point is one day's average absolute hourly error; the green line is the 30-day average. Structural improvement or degradation shows up in the smooth line, weather busts in the spikes.
Over- or under-forecasting?
Signed 30-day average bias. Positive means balancing authorities collectively predicted more demand than materialized — the conservative direction for grid operations. Across the full history, 26% of days came in over-forecast.
Seasonal norms
Average miss by calendar month across the full history — the base rate to judge any single day against. The seasonal spread is modest; what stands out more is the slow drift in the 30-day line above.
| Month | Avg daily miss | Avg bias | Days tracked |
|---|---|---|---|
| January | 1.34% | -0.68% | 341 |
| February | 1.33% | -0.50% | 311 |
| March | 1.24% | -0.30% | 341 |
| April | 1.27% | -0.49% | 330 |
| May | 1.26% | -0.64% | 341 |
| June | 1.42% | -0.62% | 330 |
| July | 1.35% | -0.45% | 334 |
| August | 1.27% | -0.36% | 310 |
| September | 1.27% | -0.37% | 300 |
| October | 1.15% | -0.64% | 310 |
| November | 1.27% | -0.77% | 300 |
| December | 1.42% | -0.75% | 310 |
Why traders and investors watch this
Forecast error is where physical volatility comes from: day-ahead power markets commit supply against these forecasts, and when actual demand comes in above them, the difference clears at whatever price scarcity demands. Under-forecast days are the raw material of real-time price spikes. The subtler signal is the bias — a persistent under-forecast lean means load keeps exceeding the models, which is the earliest statistical footprint of structural growth the forecasters haven't caught up to (unmodeled data-center energization looks exactly like this). It is also the honesty layer for the rest of the Energy section: it measures how predictable the system behind all these gauges actually is. No forward-return claim is made — this is measurement, not signal.
How this is computed—and what it cannot say
Every balancing authority files an hourly day-ahead demand forecast to EIA-930 alongside its actual demand. We sum forecasts and actuals over the same set of authorities each hour (a matched fleet), so authorities without a usable forecast never skew the comparison. A fixed rule excludes authorities whose forecast errors exceed 50% on average within a half-year file — those are broken data feeds, not forecasts; the excluded set is around 1% of national demand. The daily miss is the mean absolute hourly error over 24 complete hours; single-hour reporting glitches are interpolated with the same guard used on the demand page. History starts January 2016: EIA-930 collection launched in July 2015, and its first six months show roughly ten times the steady-state error — filing teething, not forecast skill.
This measures the accuracy of the operators' own filed forecasts in aggregate. It cannot rank individual utilities' forecasting skill, separate weather surprise from model error, or say anything about price or reliability outcomes. There is no "adjusted" forecast series, so forecasts are compared as filed, and EIA's ~30-day revision window applies to the actuals.
Sources, methodology & freshnessLast updated 2026-07-24 · Open ↓Close ↑
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How Forecast Accuracy Tracker Works
- 1The grid forecasts itself, dailyEvery balancing authority files a day-ahead hourly demand forecast into EIA-930 alongside its actuals. We compare the two — hour by hour, summed over the same BAs on both sides (a matched fleet, so coverage changes cannot fake accuracy changes).
- 2Three error reads per dayMAPE (mean absolute hourly error as % of actual) is the headline miss. Signed bias shows the lean — positive means the fleet over-forecast. Peak-hour error isolates the hour that matters most for scarcity, when a miss means scrambling for supply.
- 3Broken feeds are excluded by ruleA few small BAs file junk forecasts (one printed 10x misses). A fixed rule drops any BA whose half-year MAPE exceeds 50% from both sides of the comparison — about 1% of demand, which would otherwise inflate the national miss by a third of a point.
- 4Stats start when the data stabilizesEIA-930's first six months (late 2015) show ~10x steady-state error — collection teething, not forecast skill. Statistics begin January 2016 and the page says why.