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Load forecasting and scenario development
What You Do Today
Develop long-term load forecasts incorporating economic growth, energy efficiency, DER adoption, electrification trends (EVs, heat pumps), and weather normalization. Build multiple scenarios reflecting different future states.
AI That Applies
AI improves forecast accuracy by incorporating granular data — building permit trends, EV registration rates, industrial pipeline, and climate-adjusted weather patterns — beyond traditional econometric models.
Technologies
How It Works
The system tracks learner progress, competency assessments, and engagement patterns across the learning environment. The processing layer applies the appropriate analytical models to the structured data, generating scored outputs that surface the most actionable insights. The output is a forecast with confidence intervals, showing both the central estimate and the range of likely outcomes.
What Changes
Load forecasting moves from top-down econometric models to hybrid approaches blending econometrics with bottom-up AI analysis of adoption curves.
What Stays
Scenario design requires human judgment about what futures to plan for — economic disruptions, policy changes, technology breakthroughs — and how to weight them.
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 load forecasting and scenario development, 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 load forecasting and scenario development 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 the current accuracy of our forecasting, and how would we know if an AI model is actually better?”
They're prioritizing which operational processes to automate
your process improvement or lean lead
“Which historical data do we have that's clean enough to train a prediction model on?”
They understand the workflow dependencies that AI tools need to respect
a frontline supervisor
“Which training programs have the highest completion rates, and which have the lowest — what's different?”
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.