AI Energy Disaggregation

Non-intrusive load monitoring using machine learning for appliance-level energy tracking

The Problem

“Identify device-level energy use from one meter”

Organizations face these key challenges:

Whole-premise meter data hides which devices are driving energy use and peak demand, limiting actionable insights for efficiency and demand response.

Submetering and audits are expensive, slow to deploy, and not scalable across thousands to millions of customers; results can be outdated as equipment and behavior change.

Program targeting, M&V, and load forecasting are degraded by reliance on self-reported data and coarse segmentation, leading to wasted incentives, missed savings, and suboptimal dispatch.

Impact When Solved

60–90% reduction in end-use measurement costs versus broad submetering deployments by extracting device-level insights from existing AMI/interval data.3–8% peak demand reduction potential through device-specific DR targeting (e.g., HVAC, water heaters, EV charging) and more accurate event dispatch.10–30% improvement in segment-level peak forecasting accuracy and 1–3 percentage point uplift in verified efficiency savings through better targeting and continuous M&V.

Real-World Use Cases

Smart Grid Management and Optimization

A smart grid is like upgrading from an old landline to a modern smartphone for your electricity network. Instead of just pushing power one way from big plants to homes, the grid becomes two‑way, with sensors and software that can see what’s happening in real time, shift loads, use home batteries and solar panels, and prevent or shorten outages.

Time-SeriesEmerging Standard

9.0

AI in Energy Industry: Smart Grid Optimization and Energy Management

This is like giving the entire power system—power plants, grids, and large customers—a real‑time ‘autopilot’ that constantly predicts demand, reroutes electricity, and tunes equipment so you use less fuel, waste less energy, and keep the lights on more reliably.