THE RESULT
What the data shows
The largest computed metric is 0.2948 for Random forest; the smallest is 0.07502 for Prior baseline. Metric: average_precision (Average precision).
High load is strictly greater than the training 90th percentile (210 Wh). It is a study threshold, not a safety limit.
| model | accuracy | balanced accuracy | macro f1 | roc auc | average precision | brier |
|---|---|---|---|---|---|---|
| Prior baseline | 0.925 | 0.5 | 0.4805 | 0.5 | 0.07502 | 0.06991 |
| Logistic regression | 0.9268 | 0.5291 | 0.5366 | 0.8135 | 0.2935 | 0.06171 |
| Random forest | 0.9217 | 0.5513 | 0.5701 | 0.8565 | 0.2948 | 0.06077 |
THE METHOD
From source to answer
Threshold defined from the training prefix only, with chronological classification.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
EVALUATION
How the result was checked
First 80% of ordered observations train; final 20% test.
Fixed models; default decision threshold; no holdout tuning.
Target and input features
Target: high_load
Inputs: hour, weekday, lag_6, lag_12, lag_144
THE NEXT DECISION
What follows from the finding
Choose warning costs and evaluate on a fresh period before defining an operational alert.
Where the conclusion stops
Measurements come from one home over a limited period. Energy is Wh per recorded 10-minute interval. This is not a representative household sample; tariffs and occupancy labels are unavailable.
Related studies may reuse observations or holdouts. These are historical analyses; associations and backtests do not demonstrate commercial impact. Further model tuning needs new, untouched evaluation data.