Overview
Problem
As AI becomes increasingly integrated into household devices, users are growing concerned that it operates as a "black box" —making decisions autonomously while keeping its reasoning hidden. This lack of transparency creates a barrier to trust, leaving users feeling disconnected from the very technology designed to support them.
Project Vision
This project explores a new paradigm: using solar energy as a high-stakes medium to reimagine human-AI interaction in the home. The vision is AI as co-pilot, not autopilot — a system where automation feels like partnership, not takeover, and where users stay informed, involved, and in control.
Initial Research / Problem Validation
Why is this problem important?
Turns out, most users don’t fear smart home technology—they fear the disconnection with AI and the feeling of losing control over it.
70%
of users fear that AI will take away too much control, making them feel vulnerable or "disconnected" from daily decisions.
46%
of these consumers express high trust in the companies behind those AI systems, underscoring a gap between adoption and confidence.
Research
Interviews & Affinity Mapping
AI-driven solar energy apps are limited, so I broadened my user research to smart home automation.
I conducted storytelling-led interviews, asking participants to reflect on interactions with their smart devices. This surfaced not just pain points, but the underlying values shaping how users envision a collaborative partnership with AI. These insights were synthesized into the affinity diagram below.
X 10
Interviewees
Research
Competitive Analysis
To understand the current landscape, I analyzed existing AI-driven solar energy management apps, examining how they handle automation, transparency, energy flow visualization, and user control. This analysis reveals where current solutions fall short and where trust-based opportunities exist.
Proactive Automation:
AI manages battery discharge, pre-charges before storms, optimizes rates.
Aesthetic Minimalism:
Clean UI hides technical complexities.
Draggable Timeline:
Scrub forward to see AI's plan
Lack of Explainability:
The system shows "set-and-forget" automation without why.
Diminished Agency:
Limited user control or rule customization.
The "Wall" Effect:
AI decisions can feel opaque, creating user disconnection.
Tesla
Real-Time Energy Flow:
Instant, intuitive visualization of power routing.
Smart Forecasting:
Auto-adjusts cycles based on rates and weather.
Rigid Control:
No customization or appliance prioritization.
Opaque Optimization:
AI doesn't explain charge/discharge logic or show its plan.
Binary Control:
Limited to toggles/sliders with no custom rules.
mySigen
AI-Powered Diagnostics:
One-click troubleshooting via "Sigen AI" virtual assistant.
Advanced Data Visualization:
Sankey diagram shows complex energy flows.
Steep Learning Curve & Cluttered UI:
Unintuitive terminology for AI-driven optimization modes and cluttered design make navigation difficult.
Silent Failures:
No error messages or explanations when system fails.
"Black Box" Optimization:
AI operates autonomously without explaining its logic or allowing user input.
Synthesis
User Persona
This persona was synthesized directly from the affinity map's core insights. I translated these goals and pain points into initial feature opportunities to ensure design choices address validated human needs.
Prototype & Iteration
Digital Wireframing Interactive Low-Fi Prototype
With research-driven feature opportunities established, I built an interactive low-fidelity prototype for usability testing with potential users. This gathered feedback to validate assumptions and surface areas for refinement.
High-Fi Design - The final MVP product
The Clarity-First Dashboard
The homepage dashboard prioritizes at-a-glance scanning by keeping only essential information above the fold —
House graphic displaying energy flow status
Key metrics
Energy Balance
Upcoming scheduled tasks
Users get what they need without scrolling or interpreting complex data.
The Shared Workspace: The Energy Agenda and Negotiated Control
The Energy Agenda is a shared calendar managed by both user and AI, where user routines and AI decisions coexist.
It displays user-created events — Lifestyle Modes and Situational Overrides, alongside automated rules and Agentic AI decisions.
Each event appears as a tappable block. Tapping opens an Insight Sheet that explains the reasoning and offers options to negotiate or override.
Strategic Intent and Shared Execution
Users define high-level energy goals that serve as the primary instructions for the AI's autonomous behavior.
Users can manually create Lifestyle Modes and One-Touch Situational Overrides, save them as reusable presets, and define automated rules using If-This-Then-That conditions.
This flexibility allows them to balance long-term energy strategies with real-time lifestyle changes.
Conflict Resolution and Human Oversight
When two energy actions overlap or rules conflict, the system identifies the issue and triggers an Agent Interruption to resolve the logic conflict.
This pop-up dialog explains the conflict and asks the user to clarify their priority. This interaction model keeps the user in control and ensures they remain the final decision-maker whenever automated actions collide.
Storm Guard and Proactive Protection
The system monitors severe weather alerts to automatically generate a protective schedule within the Energy Agenda.
This proactive logic prepares the home for potential outages by prioritizing battery storage before extreme conditions arrive.
Designing With AI, About AI
Entering renewable energy without domain knowledge was challenging. I used AI tools to rapidly understand home energy management. Consulting AI throughout ensured my concepts remained grounded in near-term feasibility rather than speculative.
Designing for Inclusion
I was initially drawn to visually striking designs with bold colours and varied typography—but the options felt overwhelming. I returned to fundamentals, studying accessibility-focused design guidelines and the reasoning behind each decision. This shifted my focus from aesthetics to inclusion.
Onboarding and Trust Over Time
Design how users first encounter and learn to trust the AI. Trust builds gradually—I'd explore how the system introduces automation, earns confidence through early wins, and adjusts autonomy based on user comfort.
Multi-Stakeholder Energy Profiles
Explore how the system handles conflicting preferences between household members. Energy decisions often affect everyone. A future iteration could address shared control, conflicting preferences, and role-based permissions.