Discover Human Comfort Optimization a data-driven framework that uses human feedback, occupancy, and prediction to improve comfort and reduce energy waste in buildings.
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THE HUMAN COMFORT
OPTIMIZATION
A Simple System to Keep People Comfortable and Save Energy
The Big Problem
Why comfort systems fail
WASTED ENERGY
Empty rooms heated/cooled
PEOPLE UNCOMFORTABLE
Too hot or too cold
GUESSWORK RULES
Systems don’t listen
Insight: Old systems guess instead of listening.
❌ Buildings Waste Energy
Buildings use 40% of all energy
HVAC uses half of that
Many rooms conditioned when empty
❌ People Are Uncomfortable
75% of workers complain about temperature
People change clothes or avoid rooms
Systems guess what people want
💡 The Big Idea
“Use real human data to control comfort instead of guessing.”
Comfort improves when human data + prediction + room behavior meet.
The 3 Questions Framework
The foundation of the system
WHERE ARE PEOPLE?
- Occupancy now
- Occupancy later
WHAT DO THEY WANT?
- Too hot
- Too cold
- Just right
HOW ROOMS BEHAVE?
- Heat speed
- Cooling speed
- Sun, machines
SMART DECISIONS
Human in the Loop
How people talk to the system
PERSON
COMFORT APP
“We heard you”
COMFORT BOUNDARY
(allowed temperatures)
Key idea: People don’t control machines — they guide boundaries.
Simple Comfort Voting:
Comfort Drift
Saving energy without discomfort
Rule: No complaints → system relaxes → energy saved
Occupancy-Based Control
People = air + comfort
The 5 Key Building Blocks
How the system creates comfort while saving energy
Block 1: Know Where People Are
(Occupancy)
- Sensors count people in each room
- System knows: Empty / Half-full / Fully occupied
- Empty room → use minimum air
- Full room → increase fresh air
- No more cooling empty spaces
Block 2: Predict Where People Will Go
- System learns daily habits
- Morning arrivals, lunch breaks, evening exits
- Predicts movement before it happens
- Rooms conditioned before people arrive
- No waiting, no discomfort
Block 3: Ask Humans How They Feel
- Real comfort preferences
- Different people like different temps
- Comfort is personal, not fixed
Block 4: Let Comfort “Drift”
- If no votes → slowly relax temperature limits
- Temperature changes gradually, not suddenly
- People leave for lunch → system loosens control
- People return → comfort tightens again
Block 5: Learn Each Room
- Some heat up fast
- Some cool down slowly
- Some get sun / have machines
- Learns room behavior from real data
- Updates itself every day
The Optimization Brain
Model Predictive Control (MPC)
Think of MPC as a smart planner that looks ahead
Then it chooses the best plan
Goal: Lowest energy cost while staying comfortable
How Decisions Are Made
The optimization process from input to output
INPUTS
BRAIN
(MPC)
“What is the
cheapest way
to keep people
comfortable?”
OUTPUTS
The Feedback Loop
Why the system keeps improving
Real Results
Proven performance in actual buildings
Energy Results
Lower energy costs
Less wasted heating & cooling
Comfort Results
Dissatisfied users
80% satisfied or somewhat satisfied
Key Win
People felt better and energy use went down
The Mindset Shift
Old vs New approach to comfort
OLD SYSTEM
NEW SYSTEM
One-Screen Master Framework
If you show only ONE diagram, show this
FEEDBACK
(now + later)
BEHAVIOR
OPTIMIZATION
BRAIN
Comfort Should Be Smart, Human, and Efficient
“When buildings listen to people,
they use less energy
and people feel better.”
Sourced from; OFFICE: Optimization Framework For Improved Comfort & Efficiency by Winkler, Daniel & Yadav, Ashish & Chitu, Claudia & Cerpa, Alberto. (2020) 265-276. 10.1109/IPSN48710.2020.00030.
FAQs
What is Human Comfort Optimization?
Human Comfort Optimization is a system that improves comfort by using real human feedback, occupancy data, and prediction instead of fixed temperature rules.
It focuses on how people feel, where they are, and what will happen next while also reducing energy waste.
Why do most buildings feel too hot or too cold?
Most buildings rely on fixed schedules and guessed temperature settings.
They don’t know:
- Where people actually are
- How people feel in real time
- How each room behaves differently
This causes discomfort even when energy use is high.
How does Human Comfort Optimization reduce energy costs?
It reduces energy costs by:
- Conditioning only occupied rooms
- Relaxing comfort when no one complains
- Predicting occupancy instead of reacting late
Energy is used only when comfort truly matters.
How is Human Comfort Optimization different from smart thermostats?
Smart thermostats mostly adjust temperature based on time or motion.
Human Comfort Optimization goes further by:
- Collecting human comfort feedback
- Predicting future occupancy
- Learning how each room responds to heating and cooling
It optimizes comfort as a system, not a single device.
Can Human Comfort Optimization work in offices, homes, or smart buildings?
Yes. The framework works anywhere humans and environments interact, including:
- Offices and commercial buildings
- Homes and apartments
- Smart buildings and AI-driven systems
The core idea stays the same: listen to humans, predict needs, and adapt continuously.
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