Energy Price Surge Forecasting
Storm-surge and wave forecasting decision support for coastal and marine energy operators to reduce disruption, infrastructure damage, and personnel risk.
The Problem
“Storm-surge and wave forecasting decision support for coastal and marine energy operators”
Organizations face these key challenges:
Forecast data is spread across multiple weather, ocean, and GIS systems
Manual interpretation of surge and wave model outputs is slow and inconsistent
Operators lack asset-specific impact forecasts tied to operational thresholds
Alert fatigue occurs when all severe weather signals are treated equally
Impact When Solved
The Shift
Human Does
- •Review weather bulletins, marine forecasts, tide tables, and GIS maps for each site
- •Interpret surge and wave conditions against local operating limits and asset exposure
- •Decide on shutdowns, vessel movements, maintenance deferrals, or personnel restrictions
- •Communicate risk updates and handover notes to operations and field teams
Automation
- •No AI-driven forecasting consolidation or impact scoring is used
- •No automated prioritization of severe weather alerts across assets and routes
- •No system-generated action recommendations tied to asset-specific thresholds
Human Does
- •Approve protective actions such as shutdowns, transfer deferrals, rerouting, or evacuation steps
- •Review high-risk forecasts and recommended actions for operational feasibility
- •Handle exceptions when local conditions, asset status, or mission priorities differ from model guidance
AI Handles
- •Continuously consolidate weather, surge, wave, tide, and sensor inputs into site-specific hazard views
- •Score likely impact for each asset, route, and activity against operating thresholds
- •Prioritize alerts by severity, timing, and exposure to reduce alert fatigue
- •Generate decision-ready summaries and recommended actions for control room and field operations
Operating Intelligence
How it works
AI runs the first three steps autonomously.
Humans own every decision.
The system gets smarter each cycle.
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.
Step 1
Assemble Context
Step 2
Analyze
Step 3
Recommend
Step 4
Human Decision
Step 5
Execute
Step 6
Feedback
AI lead
Autonomous execution
Human lead
Approval, override, feedback
AI handles assembly, analysis, and execution. The human gate sits at the decision point. Every cycle refines future recommendations.
The Loop
6 steps
Assemble Context
Combine the relevant records, signals, and constraints.
Analyze
Evaluate options, risk, and likely outcomes.
Recommend
Present a ranked recommendation with supporting rationale.
Human Decision
A human accepts, edits, or rejects the recommendation.
Authority gates · 1
The system must not order shutdowns, transfer deferrals, vessel rerouting, or evacuation actions without approval from the control room lead, marine coordinator, or field operations supervisor [S1].
Why this step is human
The decision carries real-world consequences that require professional judgment and accountability.
Execute
Carry out the approved action in the operating workflow.
Feedback
Outcome data improves future recommendations.
1 operating angles mapped
Operational Depth
Technologies
Technologies commonly used in Energy Price Surge Forecasting implementations:
Key Players
Companies actively working on Energy Price Surge Forecasting solutions: