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Energy 2 min read June 11, 2026

Seven Automations That Cut Our Clients’ Energy Bills the Most

Ranked by measured impact across real installations — the automation rules that consistently deliver the largest savings.

Energy savings from automation are real, but they are unevenly distributed. Some rules save a fortune, some save pennies, and a few popular ones save nothing at all. Here is what our monitoring data consistently shows.

1. Occupancy-driven setback

Heating or cooling empty rooms is the largest single waste in most homes. Presence-based setback, with a modest three-degree drop rather than a dramatic one, delivers the biggest measured saving of any rule we deploy.

2. Solar-tracked shading

Blocking heat before it enters the glass is far cheaper than removing it afterwards. On sun-facing façades this alone can cut summer cooling load by up to a third.

3. Hot water scheduling

Most cylinders are reheated on a schedule set years ago and never revisited. Matching heating to genuine usage patterns is quick to implement and immediately visible on the bill.

4. Load shifting to cheap tariff windows

Dishwashers, laundry, EV charging and water heating moved into off-peak hours changes nothing about your day and a great deal about your monthly cost.

5. Standby elimination

Media racks, office equipment and garage circuits left permanently energised are surprisingly expensive. A goodnight scene that powers down non-essential circuits removes that draw entirely.

6. Lighting occupancy and daylight harvesting

Corridors, utility rooms and garages should never be lit by human memory. Add daylight sensing near windows and the lighting bill drops again.

7. Anomaly alerting

Circuit-level monitoring catches the failing freezer, the stuck immersion heater and the pump running twenty-four hours a day. These faults would otherwise run for months unnoticed.

You cannot reduce what you cannot see. Monitoring is not a feature — it is the prerequisite.

Start with monitoring, run it for one full month, then automate against what the data actually shows. Guessing is expensive.

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