Collaborative recommender systems for building automation

1. Collaborative Recommender
Systems for Building
Automation
Michael LeMay, Jason J. Haas, and
Carl A. Gunter
University of Illinois

2. • Motivation: Future Building Automation Systems
(BASs) will support a wide variety of control
algorithms
– Managers may not be able to determine which
algorithm is the best on their own
• Approach: Use recommender system to help
managers share opinions and quantitative
comparisons of algorithms, to result in optimal
performance
Overview

3. • Siebel Center for Computer
Science
–Centralized system permits
monitoring and control of:
• HVAC
• Card-swipe door locks
• Motion sensors
• Lighting
Sample Industrial BAS

4. • Analyze electrical consumption at
a few key points (e.g. each circuit
breaker) to determine the states
of the appliances attached to
those points
• Many possible algorithms…
– Threshold-based (incrementally
adjust appliance states based on
energy consumption changes)
– 0-1 knapsack (computationally
expensive)
Non-Intrusive Load Monitoring

5. • Increased occupant comfort relative to
configuration effort
• Decreased energy consumption
• Decreased energy cost for a given level of
consumption
• Better visibility into electrical consumption
Possible BAS Benefits

6. • BASs could deployed in a variety of
environments:
– Private homes
– Hotels
– Retail stores
– Warehouses
– Office buildings
BAS Applicability

7. • Private home with working parents and kids in
school:
– Occupied mostly from evenings through mornings and on
weekends
– Occasional guests with special requirements (e.g. extra
heat or cold, use of guest room)
• Private home with homemaker and kids at home:
– Occupied most of the day and night
• Hotel
– Similar to first scenario, but occupants change every day
or so and housekeepers stop by in middle of day
Environmental Characteristics

8. • Retail Store
– Uniformly occupied for large portions of day by large
quantities of people
– Certain parts of store have special requirements (e.g.
freezer section should be colder than other aisles)
• Warehouses
– Sparsely occupied throughout the business day by
highly-active people specially-equipped to operate in
environment (e.g. wearing coats)
– Particular sections may have special requirements,
such as a small side-office
Environmental Characteristics
(cont.)

9. • Office buildings
– Segmented into many small spaces with varying
requirements that are occupied throughout the
business day by an infrequently-changing set of
people.
– A few spaces such as conference rooms will be
unoccupied for many parts of the day, and have
various groups of people in them in other parts of the
day
Environmental Characteristics
(cont.)

10. • Lighting algorithm that turns off lights when
motion has not been detected for certain period
of time:
– In office: May turn off lights when person is relatively
still, causing annoyance.
– In retail store: Highly-effective, since shoppers rarely
stop moving
• NILM algorithm that operates using thresholds:
– Will be more effective in an environment with
appliances that can be turned on and off than one
with variable-speed motors, for example.
Effect on Control Algorithm
Effectiveness

11. • Example #1:
– Motion sensor detects occupant getting up in morning
– BAS turns on hallway and kitchen lights
– Not effective in a hotel where different occupants have different habits
• Example #2:
– Motion sensor detects occupant in room, and subsequently turns on the
lights to their maximum intensity.
– The next day, when an occupant re-enters the room, the BAS
automatically turns the lights to 2/3 of their maximum intensity.
– The occupant immediately increases the intensity to the maximum.
– The next day, the BAS uses 5/6 of maximum intensity, and the occupant is
content, as indicated by the fact that they do not subsequently increase
the intensity.
– Again, not effective in environment with rapidly-changing sets of
occupants with different preferences
More Examples

12. • Content-dependent: Recommendations made based on
similarity of new items to items previously rated by user
• Content-independent: Recommender unaware of
characteristics of items being recommended, except their
ratings from other users
– E.g. Social filtering: Generate new rating based on rating of
others, giving more weight to ratings from “similar” users
• Amazon probably uses a hybrid: Recommends items
similar to items I purchased previously, plus items
purchased by other people with similar purchase histories.
Recommender Systems

13. • Evaluate similarity
of building
managers:
• Generate
prediction:
Social Filtering

14. • Use a recommender system to recommend BAS
algorithms to building managers
• Challenging to determine in general how similar
the “contents” of algorithms are, so social
filtering is a better choice in the context of BAS
algorithms
– Building managers fill out a survey characterizing
their buildings so that their recommendations are
weighted more highly with managers of similar
buildings.
Approach

15. CollaborVation Architecture
Collaborative
Recommender

16. Animated Operational Overview
Energy
Modeler
Appliance
Usage
Detector
Occupancy
Detector Setpoint
Generator
Discomfort
Predictor
Energy
Usage
Predictor
Energy
Cost
Predictor
X10 USB
Transceiver

17. • We used the Duine recommender software for Java
to rate individual module implementations
• Provides implementations for several recommender
algorithms: User Average, TopN Deviation, etc.
• We selected Social Filtering
– All ratings of a particular algorithm are weighted by the
similarity between the building considering the algorithm
and the building that generated the rating.
– The weighted average of the ratings is the predicted
rating of the algorithm in the “querying” building.
Recommender System Prototype

18. • Five buildings: Two apartments, two small retail
stores, one industrial plant with a small office.
• Renters in apartments rate NILM algorithm #1
highly, and NILM algorithm #2 poorly
• Owner of retail store #1 rates NILM algorithm #2
poorly, and NILM algorithm #1 highly
• Owner of industrial plant rates both equally.
• Manager of store #2 requests a rating. The result?
• NILM algorithm #2 ranked lower than NILM
algorithm #1, but the rating is slightly higher than
the one provided by store #1
Recommender Example Scenario

19. • BAS algorithms may become sufficiently numerous
and complex that managers have difficulty
independently selecting the best ones for their
applications
• Recommender systems may help managers to select
appropriate algorithms
• A loosely-coupled blackboard architecture permits
BAS algorithms to be dynamically swapped when
changes are recommended
• All technologies necessary for implementation are
readily-available and reliable
Conclusion

20. • http://seclab.uiuc.edu
Thank You!