Collaborative Recommender Systems for Building Automation

Michael LeMay, Jason J. Haas, and Carl A. Gunter

University of Illinois

• 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

• Siebel Center for Computer Science–Centralized system permits

monitoring and control of:• HVAC• Card-swipe door locks•Motion sensors• Lighting

Sample Industrial BAS

• 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

• 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

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

BAS Applicability

• 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

• 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.)

• 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.)

• 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

• 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

• 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

• Evaluate similarityof buildingmanagers:

• Generateprediction:

Social Filtering

• 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

CollaborVation Architecture

CollaborativeRecommender

Animated Operational Overview

Energy Modeler Appliance

Usage Detector

Occupancy Detector Setpoint

Generator

Discomfort Predictor

Energy Usage Predictor

Energy Cost Predictor

X10 USB Transceiver

• 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

• 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

• 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

• http://seclab.uiuc.edu

Thank You!