Demand Response Manager
Performance measurement and verification
What You Do Today
Measure the actual load reduction achieved by DR events using customer baseline methodologies. Verify performance for capacity market obligations, regulatory reporting, and program cost-effectiveness analysis.
AI That Applies
AI calculates customer baselines more accurately by incorporating weather, day-of-week, and occupancy patterns, reducing measurement error that can overstate or understate actual DR performance.
Technologies
How It Works
For performance measurement and verification, the system draws on the relevant operational data and applies the appropriate analytical models. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The results integrate into the practitioner's existing workflow — presenting recommendations, flags, or automated outputs alongside their normal working context.
What Changes
Baseline calculation becomes more sophisticated and granular, enabling per-customer performance tracking rather than aggregate estimates.
What Stays
Selecting appropriate baseline methodologies for regulatory acceptance, handling disputes about performance measurement, and the judgment about what counts as "real" load reduction.
What To Do Next
This section won't tell you what your numbers should be. It will show you how to find them yourself. Every instruction below produces a real, verifiable result in your organization. No benchmarks, no projections — just the steps to build your own evidence.
Establish Your Baseline
Know where you are before you move
Before adopting AI tools for performance measurement and verification, understand your current state.
Without a baseline, you can't measure whether AI actually improved anything. You'll adopt tools without knowing if they're working.
Define Your Measures
What to track and how to calculate it
Time per cycle
How to calculate
Measure how long performance measurement and verification takes end-to-end today, then after AI adoption.
Why it matters
The most visible improvement is speed. If AI doesn't save time, question whether it's adding value.
Quality of output
How to calculate
Track error rates, rework frequency, or stakeholder satisfaction scores before and after.
Why it matters
Speed without quality is just faster mistakes. Measure both.
Start These Conversations
Who to talk to and what to ask
your VP Operations or COO
“What data do we already have that could improve how we handle performance measurement and verification?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“Who on our team has the deepest experience with performance measurement and verification, and what tools are they already using?”
They understand the workflow dependencies that AI tools need to respect
a frontline supervisor
“If we brought in AI tools for performance measurement and verification, what would we measure before and after to know it actually helped?”
They see the daily reality that AI tools need to fit into
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.