GRID EFFICIENCY INDEX● SAMPLE DAY
PEAK LOAD / MODEL3.8 kW →
Concept · self-initiated. Fictional product; all data, research and outcomes are simulated. No live services are connected.
GRID INTELLIGENCE / MIAMI CAMPUS / SIMULATED DATAPower, with
Power, with
a plan.
Turn energy signals into thoughtful schedules. Fewer peaks, smarter decisions, always under human control.
ENERGY CONSUMPTION / 24H
21.8 kWh today
MODELLED LOAD SHAPE / 06:00–23:00
−2.9 kWh optimized
UPCOMING LOADS02
Know what's
coming next.
Two high-demand devices will overlap during the evening peak.
09:00 — 11:00 PM
✳ SUGGESTED NEXT MOVE01
Move EV charging after 9 PM.
Projected savings are illustrative. Review the proposed schedule before enabling any changes.
DESIGN PRINCIPLE: INTELLIGENCE WITH AN OVERRIDENO AUTOMATIC CONTROL OF REAL DEVICESESTIMATES ARE NOT MEASURED RESULTS
Gridshift / Energy optimization
An energy planning dashboard that explains peak pricing, forecasts usage and proposes reversible schedules.
The problem
Energy dashboards show consumption but do not help households reason about tariff periods and trade-offs before changing device schedules.
Product strategy
Visualize consumption as time, money and carbon with clear assumptions; allow instant scenario comparison before approving any proposed schedule.
Proposed technical architecture
Next.js, TypeScript, TimescaleDB, event streaming, device-adapter services and scoped OAuth authorization. Simulated consumption and pricing periods. This architecture is a proposal, not a deployed backend.
Design decisions
High-contrast tactical / phosphor mint on forest black. Present price assumptions, comfort controls and a clear action authorization panel.
UX research approach
Synthetic research: 8 fictional EV/solar household profiles, 10 modeled schedule tasks. This is a fictional scenario: no real interviews or measured outcomes are claimed.
Accessibility & responsive design
Keyboard-operable controls, visible focus, readable content and responsive reflow support this concept. Reduced-motion preferences remove animated scrolling. Physical-device and assistive-technology validation remain future work.
AI-assisted workflow
Prototype peak-hour behavior, device permission boundaries, offline-state messaging and opt-out controls. AI-assisted implementation is used for prototyping and iteration; product recommendations here are deterministic local simulations.
Limitations & lessons
Offline devices and uncertain tariffs block automation. No device control occurs in the demo; physical-world actions must require consent and safe limits. The next step is real user research, production data validation and accessibility testing before any launch.
Information architecture & user flow
- User selects a preferred optimization goal
- Agent compares illustrative tariff and usage data
- Agent proposes a reversible device schedule
- User must explicitly approve supported device actions
Core views: Energy pulse overview → Peak-hour schedule planner → Consent and device-action review