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. Overview • 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

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

4. Non-Intrusive Load Monitoring • 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)

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

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

7. Environmental Characteristics • 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

8. Environmental Characteristics (cont.) • 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

9. Environmental Characteristics (cont.) • 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

10. Effect on Control Algorithm Effectiveness • 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.

11. More Examples • 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

12. Recommender Systems • 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.

13. Social Filtering • Evaluate similarityof buildingmanagers: • Generateprediction:

14. Approach • 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.

15. CollaborVation Architecture Collaborative Recommender

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

17. Recommender System Prototype • 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.

18. Recommender Example Scenario • 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

19. Conclusion • 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

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