GridLoad Outlook

Forecasts large-load and data-centre driven power demand growth to support wholesale market planning, generation and transmission investment, and trading strategy.

The Problem

“Forecast large-load and data-centre driven power demand growth for wholesale market planning”

Organizations face these key challenges:

1

Large-load growth signals are fragmented across filings, permits, press releases, and utility processes

2

Announced data-centre projects often differ materially from actual energized demand

3

Traditional load forecasts underrepresent step changes from hyperscale and industrial projects

4

Regional planning teams lack a consistent method to score project probability and timing

Impact When Solved

Improves zonal and nodal demand forecast accuracy for 1 to 10 year planning horizonsIdentifies likely versus speculative data-centre and large-load projects before full energizationSupports generation siting, transmission upgrade prioritization, and interconnection strategyEnhances wholesale trading strategy with forward demand and congestion signals

The Shift

Before AI~85% Manual

Human Does

  • •Collect large-load signals from interconnection queues, filings, permits, planning studies, and public announcements
  • •Review project evidence and judge which announced loads are credible, speculative, or delayed
  • •Update regional demand scenarios in spreadsheets and roll forecasts up to zone and node views
  • •Assess implications for generation plans, transmission priorities, and wholesale market positioning

Automation

  • •Minimal automation for basic data aggregation and spreadsheet calculations
  • •Flag simple changes in source files or public records
  • •Produce static charts or summary tables from analyst-maintained inputs
With AI~75% Automated

Human Does

  • •Approve forecast assumptions, probability thresholds, and scenario definitions for planning use
  • •Review high-impact or conflicting project cases and resolve exceptions
  • •Decide generation, transmission, interconnection, and trading actions based on forecast outputs

AI Handles

  • •Continuously monitor filings, permits, queue updates, announcements, incentives, and geospatial signals for new large-load activity
  • •Extract project attributes and score likelihood of completion, energization timing, and load ramp profiles
  • •Generate zonal and nodal demand outlooks with probabilistic scenarios across 1 to 10 year horizons
  • •Estimate impacts of projected load growth on prices, congestion, adequacy, and transmission investment priorities

Operating Intelligence

How it works

AI runs the first three steps autonomously.

Humans own every decision.

The system gets smarter each cycle.

Confidence94%
ArchetypeRecommend & Decide
Shape6-step converge
Human gates1
Autonomy
67%AI controls 4 of 6 steps

Who is in control at each step

Each column marks the operating owner for that step. AI-led actions sit above the divider, human decisions and feedback loops sit below it.

Loop shapeconverge

Step 1

Assemble Context

Step 2

Analyze

Step 3

Recommend

Step 4

Human Decision

Step 5

Execute

Step 6

Feedback

AI lead

Autonomous execution

1AI
2AI
3AI
5AI
gate

Human lead

Approval, override, feedback

4Human
6↺ Loop
AI-led step
Human-controlled step
Feedback loop
TL;DR

AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.

The Loop

6 steps

1 operating angles mapped

Operational Depth

Free access to this report