Energy Optimization Score
91/100
▲ 12.4 pts from baseline
Quantum Layer Status
QAOA
Optimizer
● ACTIVE
QRC
Forecaster
● ACTIVE
127
Qubits (sim)
IBM HBCU QC
0.003
Error Rate
▼ 22%
Financial Risk Index
LOW risk
VaR 95%: $2.14M exposure
Energy Demand Forecast · 24h
Predicted Peak
86 MW
Volatility σ
±7.4
Anomalies
1 detected
Quantum Optimization Engine
QAOA CYCLE
00/100 · iterating
GRID ALLOCATION
Solar Farm A32%
Wind Cluster B24%
Battery Bank18%
Grid Import14%
Load Shed12%
STORAGE BALANCE
74% · charging
Financial Risk Monitor
PRICE VOLATILITY · 20D
σ 0.27 ↑
INFRASTRUCTURE RISK
67/100
EXPOSURE MAP
Northeast
$3.2M
Midwest
$1.4M
South
$2.1M
West
$0.9M
Resilience Simulation
SYSTEM STATE
NOMINAL
T+ 0s
95% capacity
Primary Path
active
Failover Mesh
standby
Battery Reserve
charging
Adaptive Topology
learning
Platform I/O
What QEFO-X ingests, what it decides.
QEFO-X ingests energy, market, weather, compute, and financial data, then outputs optimized infrastructure decisions, risk scores, forecasts, and financial intelligence for energy-intensive systems.
INPUTS
Energy
- → Historical usage
- → Real-time load
- → Solar / wind generation
- → Battery & storage levels
- → Grid status
- → Outage alerts
Market
- → Electricity prices
- → Fuel prices
- → Demand charges
- → Peak pricing windows
- → Regional market signals
AI / Data Center
- → GPU & server utilization
- → Cooling load
- → Compute job schedules
- → Training / inference demand
- → Expected demand spikes
Weather
- → Temperature
- → Humidity
- → Solar irradiance
- → Wind speed
- → Storm / outage risk
Financial
- → CapEx / OpEx assumptions
- → Project financing terms
- → Insurance cost
- → Debt service
- → Hedging exposure
- → ROI targets
Risk
- → Outage probability
- → Cyber risk (PVS Prime)
- → Infrastructure degradation
- → Compliance constraints
- → Local grid reliability
OUTPUTS
Optimization Decisions
- ← When to buy power
- ← When to store
- ← When to discharge batteries
- ← When to shift compute workloads
- ← Cross-facility energy allocation
Forecasts
- ← Next-hour demand
- ← Next-day demand
- ← Price volatility
- ← AI compute load spikes
- ← Weather-driven disruption risk
Risk Scores
- ← Grid stress
- ← Energy cost risk
- ← Outage exposure
- ← Financial volatility
- ← Infrastructure resilience
Financial Intelligence
- ← Projected energy savings
- ← ROI forecast
- ← Debt-service coverage impact
- ← Investment risk
- ← Insurance pricing
Operator Actions
- ← Reduce noncritical compute load
- ← Shift AI workload to off-peak
- ← Charge battery before peak
- ← Activate backup microgrid
- ← Delay energy-intensive training
API Endpoints
- ← /forecast/demand
- ← /optimize/grid
- ← /risk/score
- ← /finance/roi
- ← /simulate/disruption
Microgrid Optimization Output — 30D Rolling
System Architecture Layers
Data Ingestion
Energy · Weather · Pricing · Macro
LIVE
AI Forecasting
LSTM · Transformer · FinBERT
ACTIVE
Quantum Optimization
QAOA · QRC · Annealing
RUNNING
Financial Modeling
VaR · Hedging · Risk Scoring
ACTIVE
Decision Output
Config · Allocation · Pricing
LIVE
Disruption Signals
AI Demand
Data Center Spike
+340% ↑
Renewable
Solar Intermittency
±62% var
Financial
Price Shock Event
$0.18/kWh
Grid
Outage Risk
3.2% prob
Quantum vs Classical Benchmarks
Forecast Accuracy (QRC)
94%
Classical Baseline (LSTM)
79%
Optimization Speed (QAOA)
3.1x
Cost Efficiency Gain
34%
Deployment Verticals
AI Data Centers
Prevent overload, cut costs, optimize PUE
Smart Cities
Real-time distributed grid control
Renewables
Storage strategy & integration
Finance
Energy hedging & investment tools
Revenue Model
SaaS
Analytics dashboards for utilities and grid operators — recurring MRR
Enterprise
Licensing to governments, energy providers & infrastructure firms
API
Real-time optimization endpoints for developers and third-party platforms
Competitive Positioning
⚛
Quantum-First
QAOA + QRC hybrid not available elsewhere
🔗
Unified Stack
Energy + Finance + AI in one system
🏛
IBM Backed
HBCU Quantum Center infrastructure access
Thesis
QEFO-X defines a new category: intelligent energy infrastructure — where physical grid systems operate as financialized, AI-optimized assets.