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

T2.3 Overview Task 2.3 “Occupancy Profiling & Activity Flow Modeling” Duration: 10 Months (M3-M12) Deliverable: contributes to D2.2 “Tertiary Local Control Hubs consumer flow modelling & profiling” (Report, Restricted, M12 – Sep. 2014) Leader: CERTH/ITI CERTH/ITI ENG TECHALIA CNet INDESIT 7 ALMENDE TUK HYPERTECH PPC ENELSI 2 4

T2.3 Objectives Thorough investigation of existing occupancy modeling methods and models (both from literature and other European projects) Investigate the flow data related to human presence and movements in tertiary buildings Define robust and credible tertiary building prosumer profiles, that will be later embodied 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 WP3 (T3.4)

Aim of the Occupancy Modeling Framework The Occupancy Flow Modeling and Prediction Framework aims to deliver: enriched individual and group-based flows models adaptable to the real-time human activity detection, utilizing privacy-conserving sensors and techniques analyze in real time and predicting human occupancy flows take full advantage on the dynamic behavior of the buildings

Overview of previous relevant work / SotA Literature (as described in the DoW): Bourgeois et al, 2004: framework modeling whole-building human activity data taking into account the impact of short term occupancy variations and other environmental factors Hutchins et al, 2007: probabilistic model that estimates the number of humans in a building using networks of occupancy sensors Barbato et al, 2009: smart home, wireless sensor network used to monitor physical parameters (like light and temperature) and presence of users at home and in each of its rooms

Overview of previous relevant work / SotA Projects (as described in the DoW): AIM: household appliance management accurate modeling of operational modes of appliances wireless sensor network to predict user behavior Deficiencies: it does not target dynamic demand side management HESMOS: integrate advance energy-efficiency tools into design & facility management process. Develop new visualization tools for building energy-related performance PEBBLE: real-time optimization and control ICT methodology in real-life buildings intelligent shaping of demand to perform generation-consumption matching occupant actions and activities directly influence the thermal behavior of the building and decision making Deficiencies of HESMOS & PEBBLE: do not analyze overall occupancy flows, semantics and complexity of day-to-day human activity and movement

Overview of previous relevant work / SotA Projects (as described in the DoW): S4EeB: audio-based building energy management monitoring and processing of sounds and noises to determine occupancy audio and occupancy sensors in adaptable and scalable network architecture Deficiencies: it does not exploit evidences of scheduled events or other occupancy flows related to the dynamic behavior of the building Adapt4EE: provisional creation of occupancy models related to business processes in buildings. Aims to: analyze human presence and movement in various enterprise buildings exploit evidences of scheduled events or other occupancy flows that may be lead to the delivery of energy-efficient modelling of the dynamic behaviour of the building deliver a set of occupancy models for simulation purposes, assisting Designer & Engineers in the early phases of energy-efficient building design Deficiencies: not targeting the dynamic demand side management occupancy flow modelling. Not investigating thoroughly tertiary building environments.

Occupancy Detection & Modeling developed by CERTH Occupancy Detection and Modeling People counting & localisation Analyze human activity and dynamically extract detailed occupancy models Comprise patterns of human presence and movement based on information from two different combined depth image sensors using human detection algorithm Occupancy models created based on spatio-temporal analysis Uses a Virtual Camera for human detection based on the Foreground estimation Several occupancy presence parameters can be extracted: Average Occupancy (mean number of user for the total period) Min and Max Occupancy (in terms of number of concurrent users in a building space/zone) Occupancy Average Values per Zone (within the room)

Occupancy Detection & Modeling developed by CERTH Human Detection Algorithm A virtual camera is introduced, “placed” on top of the monitored area The actual 2 different depth image cameras can be placed anywhere in the area Utilizing 3D information - the virtual camera provides a top-view of the area All processing is performed on depth images Avoid noise due to lightning changes 3D information can be extracted Color images are used only for visualization purposes (avoid privacy/ethical issues) Background Estimation is done before detection Rotation Estimation - calculated using 3 floor point

Occupancy Detection & Modeling developed by CERTH Color images only for visualization – not used for detection Current Status – Occupancy Tracking Output Real-time foreground detection based on depth images labeled positions of detected occupants in monitored area projected on 3D virtual camera Tracked occupancy flows on 3D virtual camera

INERTIA Occupancy Flow Modeling & Prediction Framework Part of the multi-agent-based system To allow optimal, reliable and effective coordination of the local demand side management activities

INERTIA Occupancy Flow Modeling & Prediction Framework Novel multi-sensorial framework for analyzing occupancy flows Analyze in real-time the sensors’ data, retrieve the most appropriate occupancy profile from Open Reference models and adapt it Provide occupancy flow prediction Allow optimal, reliable and effective coordination of the local demand

T2.3 Next efforts Extensive SotA analysis on Occupancy Profiling & Activity Flow Modeling, focusing on: real-time occupancy detection and modeling methods multi-sensor systems used in commercial and tertiary buildings energy-footprinting in commercial buildings Recommendations for the development of the occupancy profiling and models within INERTIA Occupancy Profiling & Activity Flow Modeling Specifications Selection of occupancy sensors and their specifications for various building zones and types that will be used in the project and during the pilot installation at CERTH premises Prepare draft version of deliverable D2.2 “Tertiary Local Control Hubs consumer flow modelling & profiling” by end of M6