Utility CFO
Forecast load growth and revenue under decarbonization scenarios
What You Do Today
Model how electrification (EVs, heat pumps), distributed generation, and energy efficiency programs will affect load shapes, revenue, and rate design over 10-30 year horizons.
AI That Applies
Load forecasting AI integrates EV adoption models, distributed solar projections, building electrification trends, and climate data to generate probabilistic load and revenue forecasts.
Technologies
How It Works
The system pulls financial data from operational systems — transactions, forecasts, actuals, and variance history. Predictive models decompose the historical pattern into trend, seasonal, and event-driven components, then project each forward while incorporating leading indicators from external data. The output — probabilistic load and revenue forecasts — surfaces in the existing workflow where the practitioner can review and act on it.
What Changes
Forecasting accounts for the energy transition's compounding effects. AI models how EV charging reshapes the load curve, how solar changes cost recovery, and how heat pumps affect winter peaks.
What Stays
You still interpret the scenarios, set the assumptions that drive the models, and make the strategic decisions about how to position the utility for a decarbonized future.
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 forecast load growth and revenue under decarbonization scenarios, 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 forecast load growth and revenue under decarbonization scenarios 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 CFO or VP Finance
“What's our current capability gap in forecast load growth and revenue under decarbonization scenarios — and is it a people problem, a tools problem, or a process problem?”
They're prioritizing which finance processes to automate first
your ERP or finance systems admin
“How would we know if AI actually improved forecast load growth and revenue under decarbonization scenarios — what would we measure before and after?”
They know what automation capabilities exist in your current stack
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.