Intelligent Light Control using Sensor Networks

1. Intelligent Light Control using Sensor Networks Vipul Singhvi1,3, Andreas Krause2, Carlos Guestrin2,3, Jim Garrett1, Scott Matthews1 Carnegie Mellon University 1 Department of Civil and Environmental Engineering 2 School of Computer Science 3 Center of Learning and Discovery

2. Maintenance and operation Construction Salary cost over building life cycle Motivation • Current built infrastructure • Trillions of dollars investment • Cost over the life cycle • Research shows potential gains from reducing operating cost and improving occupant performance • $10 - $30 billion/yr from reduced energy consumption • $20 - $160 billion/yr gained from improvement in comfort leading to better occupant performance • Reduction in energy cost related to reduced comfort & performance: Complex tradeoff optimization Life cycle building cost Sensor networks Smart monitoring and actuation can significantly reduce cost and improve occupant performance

3. Operator Motivating Scenario All lights 0-10 levels 0 10 Coordinate lighting to make everybody happy Controller Strategy to exploit natural lighting Andy Bob Louvers 5 0 5 0 Feedback 0 5 Predictive light control Louvers/ Blinds 0 10 6 0

4. Challenges • Knowing the current state • Light levels and occupants location • Capturing occupant and operator preferences & happiness • Optimization of tradeoff • Occupants happier OR save more energy

5. Indoor Environment Light levels Pervasive sensor network Wireless or Wired Desk Knowing the current state of the world • Tracking occupants • Smart tags • RFID tags • Camera tracking

6. Preferences & Happiness • Utility Theory: Framework to compare choices based on preferences • Personal preference • Attributes: Coolness, Horse Power, Mileage, COST…. • Representation complexity of utility function Toyota Corolla, New 2006 model,$30,000 Lamborghini, Second Hand 2003 model, $50,000

7. Occupant preference:Comfort Light level Utility Function Task dependent Light levels Depends on lamp setting Use sensing to learn effect of lamps on person i – Control lamp settings a to max. occupant preferences, a=(a1,…,an), aj – level of lamp j Building Operations: Occupants Andy Bob

8. 1.00 0.80 0.60 Normalized utility 0.40 0.20 0.00 100 200 300 Operating Cost Building Operations: Operator Cheaper the better • Operator preference: Cost • Operating cost • Maintenance Cost • Decreases monotonically with the energy expended • Utility function • aj , jth lamp

9. Utility Maximization : Tradeoff • Maximize system utility: Make occupants and operator happy! • a = (a1,…….an) • Scalarization technique • is the tradeoff parameter Operator Occupants

10. a2 a4 a6 a1 a4 a5 Utility Maximization: Complexity • Evaluating U(a) for combinations of all lamp setting • for just 6 lamps the total number is 106 • Evaluating argmax U(a) is also over that big space • Exponential in number of lamps! 10 levels 10 levels 10 levels 10 levels 10 levels 10 levels

11. a2 a4 a6 a1 a4 a5 Reducing Complexity • Exploit problem structure: Zoning • Distributed action selection approach (Guestrin ’03) • Exact solution to the coordination problem

12. a Open-loop controller: Coordinated Lighting Control law using Occupant utility and Coordination Graph approach

13. Test Bed • Control Schematics • 10 table lamps • 12 motes aka occupants • Size: 146 * 30 in., 7 zones 146 in. 1 3 7 4 2 6 5

14. Coordinated Lighting: Results 30% Coordinated Illumination Energy Cost Greedy Heuristic Measured utility 0.4 • Comparison to greedy approach • Each occupant comes and actuates the light • Caveat: cannot reduce the level of a already ON light • At = 0.4, reduction in comfort = 7% but reduction in energy cost = 30%

15. Coordinated Lighting • Performs significantly better than typical greedy approach • Solves the complex optimization using the structure of the problem (zoning) Predictive light control Natural Lighting Coordinated Lighting

16. Online sensing using sensor network a Closed-loop controller: Daylight Harvesting • Sense natural light levels • Actuate lamps to compensate for extra light Control law Current Light Level

17. Simulated sun using overhead lamps Sun Lamps Day Light Harvesting: Sun Simulation • Variability using the real sunlight data from Pittsburgh Measured intensities at center Real sun intensities

18. Daylight Harvesting: Utility Redefined Sun Lamps • Represents the sunlight intensity at time t and point in space x, • New utility definition • Maximization problem

19. Running the Simulations

20. Energy Cost Measured Utility Day Light Harvesting: Evaluation • Gamma values (0.01, 0.4), same setup • Gamma = 0.01, 15% of energy savings • Gamma = 0.4, 55% of energy savings • Loss in occupant utility due to too much light • Shading, Louvers Measured Utility Energy Cost

21. Day light harvesting • Builds on the coordinated lighting approach • Saves significant (~50%)energy cost during sun time • Long term sensor deployment: battery life • Sensor scheduling • Save battery life Predictive light control Natural Lighting Coordinated Lighting

22. ? ? ? Desk ? Active Sensing aka Sensor Scheduling • Spatial correlation in sunlight distribution • Temporal correlation in sunlight intensity • Use only a small number of sensor • Estimate the light levels at other times and locations When and Where to sense!

23. Active Sensing: Scheduling • Use sunlight observation (samples) to estimate the current sunlight intensity distribution • The utility formulation then changes to conditional expected utility • Choose a set of observations that yields best maximum expected utility values Sunlight Distribution Conditioned on observation

24. Active Sensing • Calculating a set of observation that maximize • More observation: better accuracy but high battery cost • Constraint the observations to a budget • Allocate strategically to max. EU

25. X1 X1 X1 X1 X1 X2 X2 X2 X2 X2 X3 X3 X3 X3 X3 X4 X4 X4 X4 X4 X5 X5 X5 X5 X5 Y1 Y1 Y1 Y2 Y2 Y2 Y3 Y3 Y3 Y4 Y4 Y4 Y5 Y5 Y5 Active Sensing: Single Sensor • Optimal solution for single sensor budget allocation in polynomial time (Krause & Guestrin ’05) • Xi where i is the time step, (5 times steps, Budget 2) • For just 2 sensors: complexity is NP-hard

26. Optimize sensor 1 X1 X1 X1 X2 X2 X2 X3 X3 X3 X4 X4 X4 X5 X5 X5 Y1 Y1 Y1 Y2 Y2 Y2 Y3 Y3 Y3 Y4 Y4 Y4 Y5 Y5 Y5 Optimize sensor 2 Active Sensing: Heuristic • Heuristic for solving multiple sensor • Coordinate ascent scheme (uses optimal solution for single sensor) • Guaranteed to improve score on each iteration, guaranteed to not perform worse than independent scheduling • Can be used for more than 2 sensors

27. Active Sensing: Results No sensing • 3 sensors, upto 10 readings per sensor in a day • Energy saving are close approximation compared to sensing continuously • Even a small number of readings (3) provides results as good as continuous Energy Cost 10 obs./sensor 1 obs./sensor Measured Utility

28. Active Sensing for Daylight Harvesting • Exploit temporal correlation in sunlight intensity to schedule sensing • Significant reduction in sensing requirement for comparable performance • Can be integrated in the coordinated lighting formulation Predictive light control Natural Lighting Coordinated Lighting

29. Zone 1 Zone 2 Zone 2 Predictive light control • Probabilistic model on mobility • People move independent of each other • Modeled using a random walk • Stay in same position • Move left, move right

30. Integrating mobility • Assuming full observability • Computing expected utility Probability of motion

31. Normalized Scale Predictive Lighting: Results Using prediction Occupant Utility Without prediction • 20 step random walk • Total utility increase of about 25% • Low values of trade-off parameter, system prefers occupants comforts Occupant Utility Energy Cost Total Utility Energy Cost

32. Conclusion • Coordinated lighting strategy • Maximizes happiness using utility maximization • Solves complex coordination problem • Day light harvesting • Exploits natural light sources using sensors • 50-70% reduction in energy consumption • Active sensing • Sensor scheduling using sunlight distribution • Substantial increase in network life time • Predictive Light control • Captures occupant mobility • Higher total utility for the system