1. Introduction
Lighting is one of the most visible parts of a building automation system.
A conventional KNX lighting installation can already provide sophisticated functions such as:
- Switching
- Dimming
- Scenes
- Presence-based control
- Time schedules
- Constant-light control
- Daylight-dependent operation
- Centralized control
However, most traditional lighting strategies still depend on predefined rules.
For example:
If presence is detected, turn the light ON.
Or:
If daylight exceeds a defined level, dim the lights.
These strategies work well, but they do not necessarily understand how people actually use a space.
Artificial Intelligence can add another layer of intelligence by analyzing occupancy, daylight, user behaviour, time, lighting levels and historical patterns.
The objective is not simply to automate lighting.
It is to make lighting adaptive, predictive and context-aware.
2. What Is AI-Based Lighting Intelligence?
AI-based lighting intelligence uses data and machine-learning techniques to understand lighting behaviour and make better decisions.
A simplified architecture is:
Occupancy
Daylight
Time
Lighting Level
User Behaviour
Room Usage
↓
KNX
↓
AI Engine
↓
Lighting Decision
↓
KNX Actuators
↓
LightsInstead of relying exclusively on fixed rules, the system can learn patterns and adapt to changing conditions.
3. Traditional KNX Lighting vs AI-Based Lighting
A traditional KNX system might operate like this:
Presence detected
↓
Light ONAn AI-enhanced system could consider:
Presence
+
Daylight
+
Time
+
Room usage
+
Previous user behaviour
+
Current lighting level
↓
AI
↓
Recommended lighting levelFor example, if a meeting room is occupied but receives sufficient daylight, the AI may recommend a lower artificial-light level.
4. KNX Provides the Control Foundation
AI does not replace KNX.
KNX can continue to handle:
- Switching
- Dimming
- Scene control
- Presence sensors
- Brightness sensors
- Actuators
- Feedback
- Manual controls
AI can sit above the automation layer and provide additional intelligence.
This is an important architectural principle:
KNX remains the reliable building-control infrastructure while AI provides higher-level optimization and prediction.
5. Why Lighting Is a Good Application for AI
Lighting behaviour is strongly influenced by human activity.
Two rooms with identical lighting equipment may have completely different usage patterns.
For example:
Meeting Room A
- Frequently occupied
- Usually used during daytime
- High daylight availability
Meeting Room B
- Rarely occupied
- Mostly used in the evening
- Limited daylight
A single fixed lighting strategy may not be optimal for both.
AI can learn these differences.
6. Data Sources for AI Lighting
An AI lighting system can use several types of data.
KNX data
- Presence
- Occupancy
- Lux level
- Dimming value
- Switching state
- Scene selection
- Manual override
- Lighting feedback
Building data
- Room schedule
- Calendar
- Operating hours
- Room type
Environmental data
- Outdoor brightness
- Solar radiation
- Weather
- Sun position
Historical data
- Previous lighting levels
- User adjustments
- Occupancy patterns
- Room usage
The more relevant context available, the better the AI can understand lighting behaviour.
7. Occupancy-Based Lighting Intelligence
Presence detection is already common in KNX.
The traditional approach is usually:
Presence detected → lights ON.
AI can make this more adaptive.
For example, the system may learn:
This room is normally occupied for 45–60 minutes when a meeting begins at 10:00.
It can then prepare the room’s lighting according to the expected use.
The objective is not merely to detect whether someone is present.
It is to understand how the space is being used.
8. Predictive Occupancy for Lighting
If historical patterns are reliable, AI can estimate future occupancy.
For example:
09:00 → Low
10:00 → High
11:00 → High
12:00 → LowThis information can support lighting decisions.
However, predictions should not override actual sensor information.
If the system expects a meeting but the room remains empty, the actual occupancy state should take priority.
Learn more about : AI-Based Occupancy Detection in KNX Buildings
9. Daylight Optimization
Daylight is one of the most important variables in intelligent lighting.
A traditional constant-light system might maintain a target illuminance by adjusting artificial lighting.
AI can extend this approach by learning how daylight changes throughout the day.
It can consider:
- Time
- Sun position
- Window orientation
- Weather
- Cloud cover
- Historical daylight behaviour
- Room occupancy
This allows the system to anticipate changes rather than reacting only after they occur.
10. Predictive Daylight Control
Consider an office with large south-facing windows.
At 09:00:
Daylight = 300 lux
At 10:00:
Daylight = 450 lux
At 11:00:
Daylight = 600 lux
AI can learn the typical pattern.
If the room requires a particular lighting level, artificial lighting can gradually adapt as daylight increases.
This can reduce unnecessary artificial-light operation.
11. Adaptive Dimming
AI can also optimize dimming rather than simply switching lights ON or OFF.
For example:
Daylight ↑
↓
Artificial Light ↓But the relationship does not have to be linear.
AI can learn:
- How quickly daylight changes
- How occupants adjust lights
- Which lighting levels are normally preferred
- Which rooms need more artificial lighting
The result can be smoother and more context-aware dimming.
12. Learning User Behaviour
One of the most interesting applications is learning from manual adjustments.
Suppose occupants repeatedly change a conference room from:
100% → 70%
when a presentation starts.
An AI system can identify this pattern.
Over time, it may learn that:
Presentation mode in this room is usually preferred at approximately 70%.
The next time the same situation occurs, the system can recommend or prepare the appropriate lighting scene.
13. Manual Override Is Important
AI should not make occupants fight the automation system.
If a person manually changes the light level, the system should respect that action.
Manual intervention can also provide valuable learning information.
For example:
AI setting: 80%
User adjustment: 60%Repeated adjustments in the same direction may indicate that the AI’s assumption should be changed.
This creates a feedback loop:
AI → User → Feedback → Learning → Improved AI
14. Personalized Lighting
Different people have different lighting preferences.
One person may prefer:
70%
while another prefers:
40%
In environments where personalization is appropriate, AI could learn preferences without requiring users to configure every parameter manually.
However, personalization should be designed carefully so that privacy and user control are maintained.
15. Scene Intelligence
KNX scenes are already extremely powerful.
A typical office might have:
- Normal
- Presentation
- Meeting
- Cleaning
- Evening
- Off
AI can help determine when particular scenes are appropriate.
For example:
Meeting starts
↓
Room occupancy increases
↓
Presentation display detected
↓
AI recognizes presentation pattern
↓
Suggest presentation lightingThe AI does not necessarily need to directly activate the scene. It can also provide a recommendation to the building automation system.
16. Lighting and Room Function
The same room can have different lighting requirements depending on its activity.
A conference room may be used for:
- Video meetings
- Presentations
- Discussions
- Training
- Informal meetings
AI can potentially recognize different usage patterns from available building data.
This allows lighting to become activity-aware, rather than simply occupancy-aware.
17. Video Conferencing and Lighting
Modern meeting rooms often have cameras and displays.
Lighting requirements for video conferencing can differ from normal office lighting.
An intelligent system could consider:
- Room occupancy
- Meeting status
- Camera operation
- Display state
- Daylight
- Existing lighting levels
and recommend an appropriate lighting scene.
For example:
Video meeting active → reduce glare and maintain suitable facial illumination.
This creates an opportunity for KNX lighting to interact with other building systems.
18. Circadian and Human-Centric Lighting
Human-centric lighting attempts to consider the effect of light characteristics on occupants.
Depending on the lighting system, this can involve:
- Intensity
- Colour temperature
- Time of day
- Occupancy
- Activity
AI can help adapt lighting behaviour based on building usage patterns.
For example:
Morning
↓
Higher activity
↓
Different lighting profile
Evening
↓
Lower activity
↓
Different lighting profileSuch systems should still follow the design objectives established by the lighting and building professionals.
19. AI-Based Lighting Energy Optimization
Lighting energy optimization is an important benefit, but it should not be the only objective.
AI can look for unnecessary operation such as:
- Lights ON in unoccupied spaces
- Excessive lighting levels
- Artificial lighting during strong daylight
- Repeated manual corrections
- Lighting schedules that no longer match occupancy
The system can then identify opportunities for optimization.
This is more sophisticated than simply applying a fixed OFF timer.
20. Detecting Unoccupied Lighting
Consider a room where lights remain ON for several hours after occupants leave.
A conventional system may already detect this through presence sensors.
AI becomes useful when occupancy behaviour is more complex.
For example:
The room is technically occupied, but has been unused for 90 minutes.
Depending on the application, AI could identify this as a potential opportunity for reduced lighting.
However, automatic decisions should respect the building’s operational requirements.
21. Lighting Usage Anomalies
AI can also identify unusual lighting behaviour.
For example:
Typical daily switching:
80 events
Current day:
420 eventsPossible explanations could include:
- Faulty presence sensor
- Incorrect automation
- User behaviour
- Configuration problem
This can be passed to the building diagnostic system.
22. AI-Based Lighting Fault Detection
Lighting intelligence can also benefit from the FDD concepts discussed in the previous article.
For example:
Command = ON
Feedback = OFFor:
Dim value = 80%
Actual response = abnormalAI can analyze these conditions alongside historical behaviour.
The important distinction is:
Lighting Intelligence focuses on optimizing operation.
Fault Detection & Diagnostics focuses on identifying problems.
The two systems can work together.
Learn more about : AI-Based Fault Detection & Diagnostics for KNX Buildings
23. AI and Shading
Lighting and shading are closely connected.
When blinds close:
Daylight decreases.
Artificial lighting may then need to increase.
When blinds open:
Daylight increases.
Artificial lighting can potentially decrease.
Therefore:
Solar Shading
↕
Daylight
↕
Artificial LightingAI can optimize both systems together.
This also creates a natural connection to the upcoming article on AI-Based Solar Shading Optimization with KNX.
24. Weather-Aware Lighting
Weather affects daylight availability.
For example:
Sunny
→ high daylight
Cloudy
→ lower daylight
Heavy rain
→ potentially much lower daylight
An AI system can combine weather information with historical daylight patterns.
This allows it to anticipate changes in artificial-light demand.
25. Lighting Intelligence in Open Offices
Open offices can have complex lighting requirements.
Different zones may have:
- Different occupancy
- Different daylight
- Different user preferences
- Different working hours
AI can analyze zone-level behaviour.
For example:
Zone A → High occupancy
Zone B → Low occupancy
Zone C → Strong daylight
Zone D → Meeting activityEach zone can receive a different lighting strategy.
26. Lighting Intelligence in Retail
Retail environments have different requirements.
Lighting may depend on:
- Customer traffic
- Store hours
- Displays
- Product zones
- Daylight
- Promotional events
AI can identify usage patterns and assist with adaptive lighting strategies.
However, lighting design objectives should always remain the primary constraint.
27. Lighting Intelligence in Hotels
Hotels can combine:
- Room occupancy
- Guest preferences
- Time
- Daylight
- Room type
- Housekeeping status
AI can help determine appropriate lighting states.
For example:
Room vacant
↓
Energy-efficient state
Guest enters
↓
Comfort lighting
Night
↓
Low-level pathway lightingKNX can provide the control infrastructure for these states.
28. AI Lighting in Smart Homes
The same concepts can apply to residential KNX installations.
AI could learn:
- Wake-up routines
- Evening lighting preferences
- Frequently used scenes
- Room occupancy
- Daylight behaviour
Instead of manually programming every situation, the system can learn recurring patterns.
For example:
The user normally reduces living-room lighting after 21:30.
The system can potentially recommend or automate that behaviour.
29. A Practical KNX + AI Lighting Architecture
A possible architecture is:
Presence Sensors
Brightness Sensors
KNX Dimmers
Lighting Feedback
Room Schedules
Weather Data
↓
KNX
↓
Data Platform
↓
AI Engine
↓
Lighting Optimization
↓
KNX Group Addresses
↓
Dimmers / Actuators
↓
LightingThe AI layer can operate alongside conventional KNX logic.
30. Rule-Based Logic + AI
A hybrid approach is often preferable.
Conventional KNX logic
Handles deterministic requirements:
If emergency condition → required lighting state.
AI
Handles optimization:
Based on occupancy, daylight and historical behaviour, reduce lighting to an appropriate level.
This ensures that critical building functions remain deterministic while AI handles more flexible optimization tasks.
31. AI Should Not Override Safety Functions
Lighting can be part of safety systems.
Emergency lighting and other safety-critical functions should follow their defined engineering and regulatory requirements.
AI should not be allowed to compromise:
- Emergency lighting
- Required escape-route illumination
- Safety procedures
- Critical operational requirements
AI should operate inside clearly defined boundaries.
32. Edge vs Cloud AI for Lighting
Lighting decisions often benefit from local processing.
Edge AI
KNX
↓
Local Gateway
↓
AI
↓
KNXAdvantages:
- Low latency
- Local operation
- Reduced cloud dependency
Cloud AI
Useful for:
- Portfolio analytics
- Multi-building comparison
- Long-term behavioural analysis
- Centralized optimization
A hybrid architecture can provide both.
33. Data Quality Matters
AI cannot compensate for poor sensor data.
Before implementing AI lighting, verify:
- Presence sensors are correctly positioned
- Lux sensors are calibrated
- Dimming feedback is reliable
- Group Addresses are correctly mapped
- Manual controls are captured
- Time synchronization is correct
Good data is the foundation of useful AI.
34. Practical Implementation Roadmap
A KNX integrator can introduce AI lighting gradually.
Step 1 — Collect Existing KNX Data
Start with:
- Presence
- Lux
- Dimming
- Switching
- Feedback
Step 2 — Analyze Historical Behaviour
Identify:
- Occupancy patterns
- Lighting usage
- Daylight patterns
- Manual adjustments
Step 3 — Identify Optimization Opportunities
Look for:
- Excessive lighting
- Unnecessary operation
- Repeated manual corrections
- Poor daylight utilization
Step 4 — Start With Recommendations
Allow AI to recommend changes rather than immediately controlling the system.
Step 5 — Validate
Compare recommendations with actual user behaviour.
Step 6 — Automate Selected Functions
Allow AI to control only validated, non-critical functions.
Step 7 — Continuously Monitor
Measure comfort, energy and user acceptance.
35. KPIs for AI Lighting
Useful KPIs include:
Energy
- Lighting energy consumption
- Operating hours
- Peak lighting demand
Comfort
- Illuminance levels
- User adjustments
- Occupant feedback
Automation
- Manual overrides
- Scene usage
- Presence detection accuracy
AI
- Prediction accuracy
- Recommendation acceptance
- Optimization impact
The goal is not simply:
Use less electricity.
The goal is:
Provide the right lighting at the right time for the right activity.
36. Common Mistakes
Mistake 1 — Optimizing Only for Energy
Lighting quality and occupant comfort remain important.
Mistake 2 — Ignoring Daylight
Artificial lighting should be considered together with available daylight.
Mistake 3 — Ignoring Manual Overrides
User adjustments provide valuable information.
Mistake 4 — Excessive Automation
Too much automation can frustrate occupants.
Mistake 5 — Poor Sensor Placement
Bad occupancy or lux data produces bad decisions.
Mistake 6 — Giving AI Unlimited Control
AI should operate within defined engineering constraints.
Mistake 7 — Replacing Reliable KNX Logic With AI
Critical and deterministic functions should remain robust even if AI becomes unavailable.
37. The Future of AI-Based KNX Lighting
The next generation of lighting systems will increasingly move from:
Scheduled
to
Occupancy-based
to
Context-aware
to
Predictive
The building could eventually understand:
- Who is using the space
- What the space is being used for
- How much daylight is available
- What lighting level is normally preferred
- How conditions are likely to change
The result is a lighting system that behaves less like a collection of switches and more like an intelligent environmental system.
38. Conclusion
KNX already provides an excellent platform for advanced lighting automation.
Artificial Intelligence can build on this foundation by learning from:
- Occupancy
- Daylight
- User behaviour
- Room schedules
- Lighting levels
- Historical operation
- Environmental conditions
This enables lighting to become more adaptive and predictive.
The progression can be summarized as:
Switching → Automation → Adaptive Control → Predictive Lighting Intelligence
The most effective approach is not to replace KNX logic with AI.
Instead:
Use KNX for reliable control and use AI to make the control strategy smarter.
For KNX integrators, this creates opportunities to move beyond traditional presence-based lighting and develop systems that understand how spaces are actually used.
The future of smart lighting is not simply turning lights on automatically. It is understanding when, where and how much light people actually need.
Read More
KNX + AI: How Artificial Intelligence Is Transforming Smart Buildings
