1. WP2 INERTIA Distributed Multi-Agent Based Framework
Task 2.3 – Occupancy Profiling & Activity Flow Modeling
CERTH/ITI

2. D2.2 “Tertiary Local Control Hubs consumer flow modelling & profiling”
T2.3 Overview
Task 2.3 “Occupancy Profiling & Activity Flow Modeling”
We are here
Involved Partners:
CERTH/ITI 7
ALMENDE 2
HYPERTECH 4
Deliverable
D2.2 “Tertiary Local Control Hubs consumer flow modelling & profiling”
(Report, Restricted,
M12 September 2013)

3. T2.3 Main achievements till now
Analysis of existing occupancy modeling methods
Analysis of Occupancy Open Reference Models available (with typical occupancy schedules for various types of buildings) and ways to utilize / enhance them within INERTIA
Defined a way to represent human presence and movements, as tracked from the multi-sensorial network, into occupancy & activity flows within spaces/zones of buildings
Implemented a prototype to evaluate proposed methodology against generated occupancy data
Define a draft specification for the definition of building prosumer profiles, that will be later embodied into the holistic flexibility models of T2.5

4. SotA Analysis Results (1/2)
Review of existing occupancy modeling methods:
Examined different algorithmic approaches in order to define the most appropriate methodology for the extraction of user profiling within the scope of INERTIA project
Diversity Occupancy Profiles
a daily profile is composed of 24 hourly values between 0 and 1, each corresponding to a fraction of the maximum peak occupancy value
weekdays and weekends are usually handled differently
Agent-based Models
agents simulate the behavior and movement of individuals based on survey or sensor data
multiple rules for each agent  probabilistically generated agents’ states
unsuitable for real-time data fusion due to high-degree of complexity
Context Sources
cost-effective system for inferring occupancy using sources such as Wi-Fi access points, system activity monitor, Instant Messaging Client, Calendar

5. SotA Analysis Results (2/2)
Multivariate Gaussian Models
hourly defined PDFs (Probability Density Functions) give the probability of an occupancy to occur within a given hour
given a starting occupancy, the probability of each possible occupancy for the next timestep is calculated using the current PDF
this method causes a great deal of pacing behavior
Markov Chain Models
Markov Chain: a system of known states where the state changes probabilistically at discrete steps
State: occupancy per room/space/zone
multiple Transition Matrices govern the state changes within different slots of time
Next State: randomly selected based only on current State and corresponding Transition Matrix probabilities

6. Proposed Methodology (1/4)
Started implementation using Markov Chain Models
Define Occupancy States per space/zone
Transition Matrix containing the probabilities of state transitions
General Architecture:

7. Proposed Methodology (2/4)
Occupancy State:
Defined as a vector in which each element represents the occupancy in each space of the building
OccRMi: number of occupants in room RMi
k: total number of rooms

8. Proposed Methodology (3/4)
Transition Matrix
Defined for each type of day (e.g. weekday, weekend) & time period (e.g. hourly)
Consider only states observed in the past and not all observable states
Initial values based on surveys, empirical data, open reference models
Calibration and training over time
Use the Closest Distance algorithm to overcome sink states (not observed states) s0 s1 … sm
p0,0
p1,0
pm,0
p0,1
pij
p0,m

9. Proposed Methodology (4/4)
Combine with Open Reference Models: use typical building models as initial values for transition matrices
Hospital Occupancy (weekdays / Saturdays)
Office Occupancy (weekdays / Saturdays)

10. Prototype to evaluate proposed method
Used 4 different spaces/zones within a building
Produced sample occupancy data (based on observations) with a 1 min timestep ti
Analysed and manipulated Raw Data to produce Transition Matrices (T) per hour
Calculated Current State, Current Time t  predict occupancy distribution within a specific hour at t+nti by raising T on the nth power
Evaluate the results of the proposed methodology against different sample data

11. Draft occupancy profile & activity flow modeling Specification
Will be based on the work of T2.1 Common Information Model – we’ll need to include below attributes:
Spatial Parameters: Define occupancy density per space/zone, specifying
min, max, average & mean values
detailed occupancy density within day (e.g. hourly distribution) / based on different time-frames (e.g. different per weekday/weekend/holiday etc., different per season – winter/summer)
Temporal Parameters: Define space/zone-specific events per specified space/zone
first arrival and last departure times, occupancy duration
Duration of Short and long absences and their typical hourly distribution within day
Typical scheduled events per space (eg. common meeting times for meeting rooms or lunch hours for kitchen)
Flow Parameters: Define occupancy flows between different building’s spaces/zones, (e.g. flow from a secretary office to the kitchen or from an developers office to the meeting room)
typical hourly distribution per flow within day

12. Deliverable D2.2 Draft ToC
Deliverable D2.2 “Tertiary Local Control Hubs consumer flow modelling & profiling” – Draft ToC
Introduction
SotA Analysis Available methods and algorithms, Advantages and Weaknesses on the scope of INERTIA,Best approach for INERTIA
Methodology
Modelling Approach Markov Chain Model Analysis, Transition Matrix Implementation
Occupancy Profile and Activity Modelling Specification Spatial, Temporal and Flow parameters, integration with CIM
Integration to Holistic Flexibility Models Information exchange among other MAS components
Conclusions

13. T2.3 Next efforts
Define, along with HYPERTECH, the definition and modeling of User-related profiles, taking as input the individual or group level preferences & operational context characteristics
Define, together with ALMENDE and HYPERTECH, the way to embody occupancy profiles produced into the holistic flexibility models of T2.5, through seamless and privacy-preserving analysis of user preferences, needs and habits
Provide the necessary implementation guidelines for the design and development of the Real-Time Occupancy Detection System in Task 3.4 and User Profiling in Task 3.5