Energy management

Knowing the bill went up
is not energy management.

Most energy monitoring stops at a number and a trend line. The useful question is narrower: which system, on which floor, at which hour, and what is it costing us against what it should. That needs metering that reconciles, attribution that goes as deep as the instrumentation allows, and a tariff that matches the invoice.

What it takes

Six layers between a meter and a decision.

Each one is where most energy management projects quietly stop — and each one is the reason the next is worth anything.

Metering
Main incomers, sub-meters, tenant meters and plant-level meters in one hierarchy, so a building total always reconciles with the parts underneath it.
Attribution
By system, by floor, by tenant and — where the instrumentation reaches — by asset. "The site used more" is not something anyone can act on, so the whole point of the layer is getting below it.
Measured or estimated
Asset-level consumption is a measured figure only where that asset has a sub-meter, or publishes power itself over the BMS as many chillers and drives already do. Where neither exists, the share is estimated by disaggregation — and shown as an estimate, never mixed in with the metered numbers.
Cost
Priced at the tariff that actually applies, per country and per utility, rather than a single blended rate applied to an entire portfolio.
Carbon
Scope 2 emissions from the same consumption figures, using published grid factors with their source recorded alongside the number.
Reporting
Board and ESG reporting generated from the live data layer, not from an export that was correct on the day someone downloaded it.
On the screen

What the estate actually looks like.

Where it goes

Consumption, cost and carbon in one place.

Electricity, water and chilled water across the estate, against the same period last year. Tariff and carbon factors are set per country, and the source of each factor is stated on the report rather than assumed.

beconixAI
Energy management dashboard showing consumption variance, cost avoided, carbon reduction and consumption by system
How you read it

Dashboards built by the people who read them.

Consumption in kWh, pump run hours, an hour-by-day heatmap, boards ranked against each other — dragged onto a grid and pointed at live data. No ticket to raise, no release to wait for, and no export that is stale by the time it is opened.

beconixAI
An energy dashboard showing consumption in kWh, pump run hours, an hour-by-day consumption heatmap and main distribution boards ranked by consumption

Interface representative. Figures are illustrative demonstration data.

In your pocket

The number, wherever the conversation happens.

Consumption, cost and carbon on the same live data as the desktop — because “how are we tracking?” is rarely asked at a desk.

  • This period against the same period last year
  • Energy, cost and emissions from one figure
  • Site by site, worst first
  • The same permissions as everywhere else
9:41
MR
ElectricityWater2026
This year against last−11.4%1.28 GWh across 14 metered sites
612kAED avoided486tCO₂e cut412MWh verified

By site

Tower One486 MWh−9.2% vs 2025
Tower Two374 MWh−14.1% vs 2025
Harbour Point272 MWh−6.8% vs 2025
West Campus191 MWh−18.0% vs 2025
Where it stops

Measuring is not the same as proving.

Consumption down 11% year on year is a fact about two numbers. It is not evidence that anything you did caused it — the weather moved, occupancy moved, and the building is not the same building it was. When a saving has to survive a finance review it belongs in measurement and verification, where a weather-normalised baseline does the comparison properly and the model has to pass published acceptance tests before its number counts.

Start with what the meters already know

If your buildings already have main and sub-meters, they can usually answer far more than they are being asked at system and building level — that part is configuration, not capital. Going below it, asset by asset, is a metering decision, and the honest first step is a survey that tells you which assets are worth instrumenting and which are not.