Sustainability & AI
Energy disaggregation
Inferring individual appliance consumption from aggregate meter data, enabling efficiency feedback without extra hardware.
Definition
Inferring the consumption of individual appliances from a building's aggregate meter signal, using signal processing or machine learning — also called non-intrusive load monitoring.
Quick reference
At a glance
- Subject
- Sustainability & AI
- Editorial status
- Editorial draft
- Definition status
- Established
- Last updated
- 21 August 2026
References
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Overview
What it means
The technique dates to Hart's foundational 1992 paper on non-intrusive appliance load monitoring, which showed that device 'signatures' could be separated from a single measurement point. Modern implementations apply machine learning to smart-meter data.
How it is used
Utilities and efficiency programmes use disaggregation to give households and facility managers appliance-level feedback without installing sub-meters, targeting the largest loads and verifying savings from retrofits and behaviour programmes.
Why it matters
Feedback is among the cheapest efficiency interventions, and disaggregation makes it scalable. It also raises the privacy side of the sustainability bargain: fine-grained consumption data reveals household behaviour, requiring careful governance.
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