AI Behind-The-Meter Optimization

Optimizes on-site load, storage, and generation schedules using tariffs and forecasts to reduce bills and peak demand.

The Problem

“Optimize behind-the-meter assets amid volatile tariffs”

Organizations face these key challenges:

High and unpredictable demand charges driven by short peak events that are hard to anticipate and mitigate manually

Fragmented control systems and data (BMS/EMS/EVSE/inverters) preventing coordinated, site-wide optimization

Operational risk and stakeholder constraints (comfort, process uptime, EV charging SLAs, battery warranty limits) that rule-based approaches cannot consistently balance

Impact When Solved

Lower peak demand and demand-charge exposure through predictive, multi-asset dispatch (often 10-30% demand-charge reduction)Improved DER utilization and ROI by increasing PV self-consumption and shifting load to lower-cost periods (typically 2-8% more self-consumption)Enhanced resilience via automated islanding/backup strategies and prioritized load shedding during outages or grid events

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.