Most smart home projects focus on automation.
Turn on a light.
Open a garage door.
Adjust a thermostat.
While that's impressive, I wanted to explore a different question:
Can AI understand what is happening inside my home?
The Vision
My long-term goal is to build an AI Home Consultant—an intelligent system capable of observing how my home operates, identifying inefficiencies, recommending automations, and ultimately helping me reduce energy consumption while improving the overall smart home experience.
Before any of that could happen, the AI needed access to something most assistants lack: context.
Not the current state of my home.
Its history.
The Technology Stack
For this project, I used:
Home Assistant running in Docker on an Orange Pi
Home Assistant Recorder for historical telemetry
MCP (Model Context Protocol)
GitHub Copilot in VS Code
My Home Assistant environment contains over 1,100 entities spanning:
Energy monitoring
Solar generation
Battery systems
Security cameras
Motion sensors
Smart lighting
Media devices
Environmental monitoring
The challenge was enabling AI to analyze this information in a meaningful way.
Giving AI Memory
Most smart home assistants can tell you what is happening right now.
I wanted my AI to answer questions such as:
What happened in my home yesterday?
When was energy consumption highest?
How active was the house?
What unusual events occurred?
Using MCP, I connected GitHub Copilot directly to Home Assistant's historical data, allowing it to query:
State history
Energy statistics
Device activity
Security events
Occupancy signals
For the first time, the AI could look backwards instead of only reacting to the present.
The First Daily Home Intelligence Report
Once connected, I asked a simple question:
Tell me the story of my home over the last 24 hours.
The response surprised me.
Instead of returning raw telemetry, the AI generated a narrative describing:
Solar production patterns
Battery charging and discharging cycles
Front door activity
Motion events
Lighting behaviour
Security status
Energy trends
It transformed hundreds of sensor readings into a coherent explanation of what happened throughout the day.
The house was no longer just generating data.
It was explaining itself.
An Unexpected Discovery
One of the most interesting outcomes was that the AI didn't simply summarize information.
It started questioning it.
During analysis, it identified inconsistencies in the energy telemetry and highlighted sensors that required validation.
This revealed something important:
Before AI can optimize a smart home, it must first learn which data can be trusted.
The system wasn't just acting as an assistant—it was becoming an analyst.
What Phase 1 Achieved
By the end of Phase 1, I successfully built the foundation of an AI Home Intelligence Layer.
The AI can now:
Access historical Home Assistant data
Analyze energy telemetry
Review security and occupancy events
Detect patterns across multiple systems
Generate daily intelligence reports
Most importantly, it can answer a question that traditional smart home systems cannot:
What happened in my home yesterday?
What's Next?
Phase 2 shifts the focus from understanding to optimization.
Instead of asking:
What happened?
We'll start asking:
What should I improve?
The next phase will focus on:
Energy waste detection
Occupancy analysis
Automation recommendations
Home efficiency scoring
AI-generated optimization opportunities
The journey from smart home to intelligent home has officially begun.
