---
title: 'Household Report'
description: Plain-language gas analytics for a home, school or office — consumption dynamics, breakdown, detected losses, a 12-month forecast and savings tips.
section: AI Analytics
weight: 6
related:
  - ai-analytics/node-reports/emissions-and-energy
---

A clear report on how much gas a home (or a school or office) consumes and where it goes. It is written for the property owner and the site administrator — without complex terms, with practical tips on how to save. It can be exported to PDF and handed to the customer as a leaflet.

## What it shows

| Section | What you will learn |
|---|---|
| The gist in brief | how much gas was used, for what amount, and how much can be saved |
| About the site | building type, area, number of occupants, tariff, and the gas meter itself |
| By month | how consumption changed over the year, with seasonality visible |
| By hour of day | when the site uses more gas — morning, evening, night |
| Where the gas goes | shares: heating, hot water, cooking, other |
| Where it is lost | excess consumption (overheating, constant burning, leaks) |
| Yearly forecast | how much gas and money will be spent under the current regime |
| Tips | what to do now, in half a year, in a few years |
| Environmental effect | how much less CO₂ with savings (expressed in "trees") |

## How it works

The report takes hourly gas-consumption data for the selected period and aggregates it by day and month. Then:

- **Where the gas goes.** If the site card lists equipment (boiler, stove, water heater), consumption is distributed only across what is actually present. If there is no such data, a typical breakdown for the building type (house, school, office, apartment building) is used instead.
- **Where it is lost.** The report looks at the "background" consumption (the minimum level that burns almost all the time) and at sharp peaks. Everything above a reasonable norm is counted as losses — but no more than 30 % of total consumption, so the estimate is not overstated.
- **Forecast and savings.** From the average daily consumption the annual figure is estimated and a 15 % savings target is proposed. The money saved can be shown with accumulation at a bank interest rate over 10 years.

Area, number of occupants, and the presence of equipment are taken from the site card; if they are not there, they come from the run form with reasonable defaults.

## Run parameters

| Parameter | Default | What it means |
|---|---|---|
| Metering node # | required | which gas meter to analyse |
| Period from / to | required | the timeframe to calculate for |
| Building type | house | house, school, office, or apartment — sets the typical breakdown |
| Area, m² | 100 m² | heated area |
| Number of people | 4 | residents, staff or students at the site |
| Gas tariff | 6.95 per m³ | gas price |
| Bank rate | 18 % per annum | the interest rate for compounding the savings |
| AI analyst commentary | off | add a plain-language analysis written by AI |

## How to read the result

Start with the "The gist in brief" block: it shows the amount for the period, the yearly forecast, and how much can realistically be saved.

- A large share of **heating** in winter is normal.
- High **background** consumption at night in a residential home is grounds to check for a leak or a constantly running burner.
- The **"Where it is lost"** section shows the money being wasted and what to do about it.

What to keep in mind:

- The "where the gas goes" breakdown is an estimate. Exact shares can be obtained only with separate meters on each appliance.
- The 15 % savings and the effect of insulation are typical benchmarks; the real result depends on the condition of the building.
- If a large "excess" consumption is visible, it is worth additionally checking the node with the [Metering Bypass](/en/platform/v3/ai-analytics/node-reports/metering-bypass) report.