Traditional Post-Occupancy Evaluation (POE) uses questionnaires given to occupants 6–12 months after moving in. But people often cannot accurately recall how frequently they used each space, or may have unconsciously adapted their behavior to work around spatial limitations. Smart sensors in SmartInterior capture real data without relying on memory.
Core Data Sources for Data-Driven SmartInterior
- Occupancy Sensor Data: which rooms are used, at what times, for how long, and at what density 2. Smart Plug Energy Data: which appliances are actually used, which are rarely touched 3. Air Quality Sensors: which rooms consistently show high CO2 levels (= insufficient ventilation or more occupants than designed for) 4. Smart Lighting Log: which lights are turned off immediately each time they are turned on (= wrong type or placement) 5. HVAC Runtime Data: which rooms require unusually long HVAC run times (= insufficient insulation or high solar heat gain)
Common Insights After 3 Months of Data
Real examples from SmartInterior projects: - Child’s bedroom: designed as a study space, but Occupancy Data shows primary use 22:00–01:00, and Smart Plug Data shows 90% of energy from a gaming PC → adjust lighting for gaming (dim after 21:00 + RGB Ambient) and add Acoustic Panels to avoid disturbing parents’ bedroom - Living room: HVAC running 18 hours per day, but Occupancy Sensor shows the room is empty 60% of that runtime → install Zone Control + Presence Sensor, immediately saving 40% of HVAC cost - Kitchen: Smart Lighting Log shows the Island Pendant Light is turned off within 5 minutes every time it is switched on → too bright, adjusted from 4,000K Cool White to 2,700K Warm White at 300 lux → usage normalized
Data-to-Design Loop: The Continuous Improvement Cycle
- Detect: system identifies anomalous patterns (barely-used rooms, appliances consuming more than expected) 2. Understand: analyze why (space not matching real needs? inconvenient access? too hot?) 3. Intervene: adjust lighting, add storage, rearrange furniture, or modify HVAC zones 4. Measure: track whether the change shifted the data in the desired direction
Outcomes of Data-Driven Approach
Homes that complete a 12-month Data-Driven Refinement process achieve: - 15–25% improvement in Space Utilization (previously unused rooms returning to active use) - Additional 10–20% energy reduction on top of initial optimization - Occupants report 30% higher Satisfaction Score when the home adapts to their real life - Significantly reduced need for Renovation within the first 5 years, because the home adapts through data