THE RESULT
What the data shows
The largest computed metric is 111.5 for 0.0; the smallest is 87.12 for 1.0. Metric: mean_wh (Wh per 10-minute interval).
Weekday 0 is Monday. Changes in weather and household behavior are not controlled.
| weekday | intervals | median wh | mean wh | p90 wh |
|---|---|---|---|---|
| 0 | 2,778 | 60 | 111.5 | 280 |
| 1 | 2,880 | 60 | 87.12 | 140 |
| 2 | 2,880 | 70 | 89.93 | 140 |
| 3 | 2,880 | 60 | 90.43 | 150 |
| 4 | 2,845 | 60 | 104.6 | 260 |
| 5 | 2,736 | 70 | 106.2 | 240 |
| 6 | 2,736 | 60 | 94.92 | 150 |
THE METHOD
From source to answer
Calendar-day categories with robust energy summaries.
The Python source exposes this study’s transformations. The complete project download includes shared preparation and evaluation routines.
THE NEXT DECISION
What follows from the finding
Use the observed schedule as a hypothesis to compare with occupancy and appliance logs.
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.