Energy & Utilities · Energy Efficiency & Demand Response

Energy Efficiency Program Targeting & EM&V

Enhances→Stable

Available Now

Production-ready. Commercial solutions exist and organizations are actively deploying.

Trajectories describe the observable direction of human effort — not a prediction about specific roles, headcount, or individual careers.

What You Do Today

Design DSM/EE programs, market them to customers through blanket campaigns, manage rebate processing, and conduct evaluation, measurement, and verification (EM&V) to prove savings to regulators. Pass the TRC (Total Resource Cost) and UCT (Utility Cost Test) benefit-cost tests to maintain program funding.

AI Technologies

ML Segmentation (Program Propensity Scoring by Customer)Predictive Analytics (Demand Response Dispatch Optimization)ML Forecasting (Avoided Cost and Benefit-Cost Calculation)Anomaly Detection (Free Rider and Spillover Estimation)Automated Reporting (EM&V Impact Evaluation Filing)

Roles Involved

Who works on this

Demand Response ManagerEnergy Efficiency ManagerUtility PlannerData Analyst

Manager/SupervisorIndividual Contributor

How It Works

ML identifies highest-propensity households and businesses for each program based on usage patterns, building characteristics, and participation history. Replaces blanket marketing with precision targeting.

What Changes

Program marketing becomes targeted rather than mass-mailed. EM&V moves from sampling-based to census-based analysis using AMI data. Free-rider estimation improves with ML counterfactual analysis.

What Stays the Same

Program design creativity and regulatory advocacy. Convincing a commission to approve an innovative program requires storytelling, stakeholder engagement, and political savvy that no model provides.

Cross-Industry Concepts

Customer Segmentation Personalization Regulatory Compliance

Evidence & Sources

•ACEEE program evaluation resources
•DOE Uniform Methods Project
•State EM&V frameworks

Sources listed are directional references, not formal citations. Verify against primary sources before using in business cases or presentations.

Last reviewed: March 2026

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 energy efficiency program targeting & em&v, document your current state in energy efficiency & demand response.

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Map your current process: Document how energy efficiency program targeting & em&v works today — who does what, how long each step takes, and where the bottlenecks are. Use your SCADA/EMS data to establish a factual baseline.

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Identify the judgment calls: Program design creativity and regulatory advocacy. Convincing a commission to approve an innovative program requires storytelling, stakeholder engagement, and political savvy that no model provides. — these are the boundaries AI won't cross. Know them before you start.

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Check your data readiness: AI tools for energy efficiency & demand response need clean, accessible data. Check whether your SCADA/EMS has the historical data, integrations, and quality to support ML Segmentation (Program Propensity Scoring by Customer) tools.

Without a baseline, you can't tell whether AI actually improved energy efficiency program targeting & em&v or just changed who does it.

Define Your Measures

What to track and how to calculate it

system reliability (SAIDI/SAIFI)

How to calculate

Measure system reliability (SAIDI/SAIFI) for energy efficiency program targeting & em&v before and after AI adoption. Pull from your SCADA/EMS.

Why it matters

This is the most direct indicator of whether AI is adding value to energy efficiency & demand response.

generation efficiency

How to calculate

Track generation efficiency using the same methodology you use today. Don't change how you measure just because you changed how you work.

Why it matters

Speed without quality is just faster mistakes. Measure both together.

When to check: Check after 30 days of consistent use, then quarterly.

The commitment: Give new tools at least 30 days before judging. The first week is always awkward.

What NOT to measure: Don't measure AI adoption rate as a goal. Measure outcomes. If the tool helps with energy efficiency program targeting & em&v, people will use it.