US electricity net generation · United States · 12 months ahead
2027-05: the model assigns an 80 percent probability to 350,484 to 374,292 million kilowatthours
Point estimate 360,015 million kilowatthours from Drift, against 354,691 million kilowatthours observed in 2026-05. drift beats last_value on out-of-sample MAE by 13.6 percent over 36 cutoffs (minimum 24, margin 5 percent).
Fan chart
- Quantiles
- p5 346,161 · p10 350,484 · p25 354,798 · p50 363,917 · p75 368,582 · p90 374,292 · p95 379,828
- Source
- empirical quantiles of 36 in-window 12-step residuals, in log space; no normality assumed
Immutable fields
- Key
- us-electricity-net-generation:USA:12
- Id
- 0bb7e322-b171-4379-a926-2bbe1f1fb4a8
- Created
- 2026-09-07 22:16 UTC
- Data cutoff
- 2026-09-07 22:16 UTC
- Origin
- 2026-05 = 354,691 (obs 245396)
- Target period
- 2027-05 (365 days)
- Point
- 360,015 million kilowatthours
- Interval
- 350,484 to 374,292 at 80 percent
- Model
- drift models_v1.targets_v1
- Baseline
- last_value
- Snapshot
- 4d454002289e02078827926c6c3b8d6d13800a4943d3db41e9aabcbd54e742c2
- Data mode
- ingested_at
- Status
- active
Statistical association, not causal
Drift uses only the target's own history; there are no driver contributions.
Not yet realised
The observation for 2027-05 has not been ingested. The weekly forecast.evaluate job attaches it when it arrives; the record above does not change.
What the forecast takes for granted
- No structural break in us-electricity-net-generation between the data cutoff and the target period.
- The source keeps publishing the series on the same definition; a rebasing or redefinition invalidates the comparison.
- The average historical change per period continues.
What would show this forecast wrong
- If the first published value of us-electricity-net-generation (USA) for 2027-05 is below 350,484 million kilowatthours or above 374,292 million kilowatthours, the 80 percent interval is falsified.
- The model assigns a 10 percent probability to a value below 350,484 million kilowatthours and a 10 percent probability to a value above 374,292 million kilowatthours; interval misses should occur about one time in five over many forecasts, and a run of misses well above that rate falsifies the calibration.
- The point forecast is above the last observed value (354,691 million kilowatthours); a realised value at or below that level falsifies the direction call.
Scorecard for this target and horizon
| Model | Status | Cutoffs | MAE | RMSE | Direction | 80% coverage | Skill vs last value |
|---|---|---|---|---|---|---|---|
| Drift models_v1.targets_v1 | champion | 36 | 9,312 | 11,452 | 78% | 89% | 13.6% |
| Holt (exponential smoothing with additive trend) models_v1.targets_v1 | challenger | 36 | 24,565 | 28,646 | 42% | 89% | -128.0% |
| Last value models_v1.targets_v1 | baseline | 36 | 10,776 | 13,434 | 0% | 89% | 0.0% |
| Linear trend models_v1.targets_v1 | challenger | 36 | 30,891 | 36,409 | 67% | 83% | -186.7% |
| Ridge regression on lags and drivers models_v1.targets_v1 | challenger | 36 | 9,738 | 12,462 | 72% | 92% | 9.6% |
| Seasonal naive models_v1.targets_v1 | challenger | 36 | 10,776 | 13,434 | 0% | 89% | 0.0% |
Drift
Drift
Purpose. Point and 80 percent interval forecasts of us-electricity-net-generation (USA) 1, 3, 6, 12 months ahead. Owner: Forecast Lab (HV 3.0).
Method
The forecast extends the mean first difference of the whole training window from the last value: last value plus horizon times the average change. The series is modelled in natural logarithms and transformed back, so intervals are asymmetric in level terms.
Uncertainty
Intervals are empirical: the model is refitted at earlier origins inside the training window, the realised errors at the same horizon are collected, and the interval is the 10th to 90th percentile of those errors around the point forecast. No normality is assumed.
Training and evaluation
Training window: 641 periods, 1973-01-01 to 2026-05-01. Evaluation: rolling-origin backtest over 36 cutoffs using pseudo real-time data (the earliest ingested value per period; no vintage archive exists for this series, so revisions between the cutoff and today are not reproduced).
Known limitations
- Structural breaks after the cutoff are not modelled.
- Driver contributions are statistical associations, not causal effects.
- Promotion to champion requires beating the last-value baseline on out-of-sample MAE by at least 5 percent over the minimum number of cutoffs; otherwise the baseline stays champion.
- Retraining: every scheduled forecast.run refits on the data then available; backtests re-run from /admin/forecasts.
Supersedes
This is the first record for its key.