Utility Planner
Developing load forecasts and demand projections
What You Do Today
Project future electricity demand based on population growth, economic trends, electrification (EVs, heat pumps), energy efficiency programs, and behind-the-meter generation.
AI That Applies
AI integrates multiple data sources — economic indicators, building permits, EV adoption curves, weather trends — to produce probabilistic load forecasts with confidence intervals.
Technologies
How It Works
For developing load forecasts and demand projections, the system draws on the relevant operational data and applies the appropriate analytical models. 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 forecasts with confidence intervals — surfaces in the existing workflow where the practitioner can review and act on it.
What Changes
Forecasts are more granular and accurate. AI models the impact of EV adoption or a data center moving in at the substation level, not just system-wide.
What Stays
Interpreting forecasts and making planning assumptions requires engineering judgment. Models can't predict policy changes, economic disruptions, or customer behavior shifts.
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 developing load forecasts and demand projections, 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 developing load forecasts and demand projections 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's our current capability gap in developing load forecasts and demand projections — and is it a people problem, a tools problem, or a process problem?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“What's the biggest bottleneck in developing load forecasts and demand projections today — and would AI address the bottleneck or just speed up something that's already fast enough?”
They understand the workflow dependencies that AI tools need to respect
Check Your Prerequisites
Confirm readiness before you invest
Check items as you confirm them.