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
“AI Behind-The-Meter Optimization for Cost, Peak, and Resilience Management”
Organizations face these key challenges:
Demand charges and time-of-use tariffs are difficult to optimize manually
Forecast uncertainty for load, solar, weather, and prices degrades static schedules
Flexible assets have many operational constraints such as comfort, process windows, and battery degradation
Operators lack a unified optimization layer across BMS, EMS, DERMS, and edge devices
Emergency scenarios are too numerous and complex to simulate manually at sufficient depth
Distributed batteries are often underutilized because coordination is operationally complex
Legacy systems expose inconsistent telemetry and control interfaces
Regulatory, safety, and reliability requirements limit acceptable automation behavior
Impact When Solved
The Shift
Human Does
- •Review interval usage, solar output, and tariff schedules to identify cost drivers.
- •Set static operating schedules for batteries, EV charging, and flexible loads.
- •Coordinate manually across facility systems to reduce peaks and maintain comfort.
- •Adjust plans during unusual weather, occupancy changes, or grid events.
Automation
- •No AI-driven forecasting or optimization is used in the legacy workflow.
- •Basic alarms or rule triggers flag obvious threshold breaches.
- •Simple reports summarize historical consumption and peak demand patterns.
Human Does
- •Approve operating objectives, comfort limits, charging priorities, and resiliency policies.
- •Review recommended dispatch plans and authorize exceptions for business-critical conditions.
- •Handle edge cases such as outages, maintenance constraints, or conflicting site priorities.
AI Handles
- •Forecast site load, solar generation, EV demand, and tariff exposure at high time resolution.
- •Continuously optimize battery, load, and charging schedules to reduce energy spend and peaks.
- •Coordinate multi-asset dispatch across on-site generation, storage, and flexible loads.
- •Monitor real-time conditions and automatically adjust schedules as forecasts or grid conditions change.
Operating Intelligence
How AI Behind-The-Meter Optimization runs once it is live
AI runs the operating engine in real time.
Humans govern policy and overrides.
Measured outcomes feed the optimization loop.
Who is in control at each step
Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.
Step 1
Sense
Step 2
Optimize
Step 3
Coordinate
Step 4
Govern
Step 5
Execute
Step 6
Measure
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI senses, optimizes, and coordinates in real time. Humans set policy and override when needed. Measurements close the loop.
The Loop
6 steps
Sense
Take in live demand, capacity, and constraint signals.
Optimize
Continuously compute the best next allocation or action.
Coordinate
Push those actions into systems, channels, or teams.
Govern
Humans set policies, objectives, and overrides.
Authority gates · 1
The system must not change resiliency reserves, backup power priorities, or load-shedding rules without approval from the responsible energy or facility operator. [S3][S5][S7]
Why this step is human
Policy decisions affect the entire operating envelope and require organizational authority to change.
Execute
Run the approved operating loop continuously.
Measure
Measured outcomes feed back into the optimization loop.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in AI Behind-The-Meter Optimization implementations:
Key Players
Companies actively working on AI Behind-The-Meter Optimization solutions:
Real-World Use Cases
AI emergency scenario simulation for nuclear plant response planning
AI runs thousands of nuclear emergency what-if drills on a computer and helps choose the best response before a real problem happens.
EV and battery scheduling for site energy autonomy
AI and optimization decide when a site should charge or use electric vehicles and stationary batteries so the building can rely more on its own energy and less on the grid.
Weather-informed solar integration control for smart grids
The grid uses weather forecasts and smart controls to predict how much solar power will show up, then adjusts equipment so the lights stay steady even when clouds pass by.