Sign in

AI Load Control Devices

The Problem

“Reduce peak demand without harming customers”

Organizations face these key challenges:

1

Peak demand volatility causes high capacity charges, congestion costs, and reliability risk, but available flexibility is uncertain and hard to dispatch precisely.

2

One-size-fits-all control strategies create customer discomfort, opt-outs, and rebound peaks, reducing program effectiveness and increasing churn.

3

Slow, inaccurate measurement and verification and fragmented device ecosystems (thermostats, water heaters, EVSE, batteries) increase operational burden and limit scale.

Impact When Solved

15–35% higher realized kW reduction per event through predictive, customer-specific control and reduced opt-outs.20–40% reduction in program operating costs via automated dispatch, anomaly detection, and faster M&V/settlement.$50–$150 per kW-year avoided capacity/peak procurement costs, enabling multi-million-dollar annual savings at 50–200 MW portfolio scale.

The Shift

Before AI~85% Manual

Human Does

  • •Review peak forecasts and decide whether to call a demand response event
  • •Select customer segments and device groups for static load control schedules
  • •Coordinate event timing, customer notifications, and manual program operations
  • •Investigate customer complaints, opt-outs, and device performance issues

Automation

  • •Produce basic feeder or system load forecasts from historical usage and weather
  • •Apply fixed rule-based cycling schedules to enrolled devices during events
  • •Flag simple threshold breaches for operator review
  • •Generate coarse baseline and event performance calculations after dispatch
With AI~75% Automated

Human Does

  • •Approve dispatch strategies, comfort guardrails, and program objectives
  • •Decide how much flexibility to commit for peak reduction, congestion relief, or market participation
  • •Review exceptions such as abnormal device behavior, customer escalations, or underperformance

AI Handles

  • •Forecast near-term load, flexibility, and peak risk at device, premise, feeder, and portfolio levels
  • •Optimize and execute customer-specific control actions to hit kW targets while minimizing discomfort and rebound
  • •Monitor telemetry in real time and triage anomalies, opt-out risk, and device availability issues
  • •Estimate customer-specific baselines and counterfactual consumption for rapid measurement and verification

Operating Intelligence

How AI Load Control Devices runs once it is live

AI runs the operating engine in real time.

Humans govern policy and overrides.

Measured outcomes feed the optimization loop.

Confidence94%
ArchetypeOptimize & Orchestrate
Shape6-step circular
Human gates1
Autonomy
67%AI controls 4 of 6 steps

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.

Loop shapecircular

Step 1

Sense

Step 2

Optimize

Step 3

Coordinate

Step 4

Govern

Step 5

Execute

Step 6

Measure

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI senses, optimizes, and coordinates in real time. Humans set policy and override when needed. Measurements close the loop.

The Loop

6 steps

1 operating angles mapped

Operational Depth

Free access to this report