# Find the half hour that matters.

A week of building measurements becomes one inspectable energy event, with the original readings and the limits of the conclusion attached.

By Reific. Published 2026-09-15.
Category: 01 / Building energy / Public-data replay. Read: [article](/research/building-demand).
Question: Which three consecutive 10-minute readings have the highest total appliance energy in the first seven complete days, and what can we conclude?


## Start with a question the data can answer.

We took the first seven complete days from a published building-energy dataset: 1,008 measurements at ten-minute intervals. The question is deliberately narrow: find the three consecutive readings with the greatest combined appliance energy. This gives an agent a concrete event to inspect without asking it to perform arithmetic over every row.

The selected period and method are explicit. The replay enumerates all 1,006 candidate windows, ranks their sums, and preserves the source rows for the maximum. There is no language-model call, learned anomaly detector, or customer deployment behind this example.

## The result: 2,390 Wh in three readings.

The largest sum is 560 + 1,080 + 750 = 2,390 Wh, at the source timestamps 16 January 2016, 18:40, 18:50, and 19:00. Those three ten-minute energy intervals correspond to an average power of 4,780 W. The original CSV lines are 732–734, with the header counted as line 1.

The timestamp labels span twenty minutes; the three energy measurements each describe a ten-minute interval. The source does not say whether a timestamp labels the start or end of that interval, so we preserve its labels rather than inventing an exact interval boundary or timezone.

## A maximum is an observation. A fault needs more evidence.

This window is the maximum in this week. That alone does not make it abnormal, wasteful, or faulty. An aggregate meter cannot identify the appliance responsible. A credible next step is to inspect device meters, schedules, and intervention records before recommending action.

The evidence packet carries that limit alongside the readings. Its usefulness is the audit trail: question, coverage, method, values, source rows, and the information still needed for a stronger conclusion.

## Compare against a sensible simple baseline.

The complete 29-field CSV slice contains 250,068 tokens. Merely selecting the timestamp and appliance-energy columns reduces that to 16,838. The complete evidence JSON contains 529 tokens: 16,309 fewer than the projection, a 96.9% reduction for this specific input payload.

A short SQL query or Python aggregation can compute this result too. This example demonstrates the evidence interface and its exact payload size. It does not establish an advantage over a tool-using agent, better task resolution, or lower total agent cost.

## Sources

1. [Candanedo (2017), Appliances Energy Prediction — UCI dataset and attribution](https://archive.ics.uci.edu/dataset/374/appliances+energy+prediction)
2. [Dataset DOI: 10.24432/C5VC8G](https://doi.org/10.24432/C5VC8G)
3. [Creative Commons Attribution 4.0 International](https://creativecommons.org/licenses/by/4.0/)

Reproduction files: [measurements and checksums](/evidence/replay.json), [script](/evidence/replay.py), [source bundle](/evidence/replay-bundle.zip).