Building Multi-occupancy Analysis and Visualization Through Data Intensive Processing
A novel Building Multi-occupancy Analysis & Visualization through Data Intensive Processing techniques is going to be presented in this paper. Building occupancy monitoring plays an important role in increasing energy efficiency and provides useful se
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Information Technologies Institute, Centre for Research and Technology Hellas, 6th Km Charilaou-Thermi, 57001 Thermi-Thessaloniki, Greece {djoannid,ptropios,krinidis, Dimitrios.Tzovaras}@iti.gr 2 Computer Engineering and Informatics, University of Patras, Rio, Patras, Greece [email protected]
Abstract. A novel Building Multi-occupancy Analysis & Visualization through Data Intensive Processing techniques is going to be presented in this paper. Building occupancy monitoring plays an important role in increasing energy efficiency and provides useful semantic information about the usage of different spaces and building performance generally. In this paper the occupancy extraction subsystem is constituted by a collection of depth image cameras and a multi-sensorial cloud (utilizing big data from various sensor types) in order to extract the occupancy per space. Furthermore, a number of novel visual analytics techniques allow the end-users to process big data in different temporal resolutions in a compact and comprehensive way taking into account properties of human cognition and perception, assisting them to detect patterns that may be difficult to be detected otherwise. The proposed building occupancy analysis system has been tested and applied to various spaces of CERTH premises with different characteristics in a real-life testbed environment. Keywords: Big data analysis Building occupancy Human presence Building occupancy visualization
Occupancy extraction
1 Introduction Knowing the true occupancy, the presence or the actual number of occupants of a building at any given time is fundamental for the effective management of various building operation functions ranging from security concerns to energy savings targets, especially in complex buildings with different internal kind of use [1–4]. The accurate definition of occupancy is the amount of people per building’s spaces at any given time. Furthermore the influence that the occupants’ actions have in the indoor environment [5], including those related to their business processes can also be added to the definition. Occupant’s locations within the building varies throughout the day, therefore it is difficult to characterize the number of people that occupy a particular space © IFIP International Federation for Information Processing 2016 Published by Springer International Publishing Switzerland 2016. All Rights Reserved L. Iliadis and I. Maglogiannis (Eds.): AIAI 2016, IFIP AICT 475, pp. 587–599, 2016. DOI: 10.1007/978-3-319-44944-9_52
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and for what duration because human behavior is considered stochastic in nature [6]. Due to the random nature of individuals’ behavior and challenges accessing accurate data, current studies include the creation of deterministic schedules where a standard workday profile is the same for the whole workweek and both weekend days have the same profile [7]. There are numerous techniques to detect space occupancy and even track their movements, which can be found in the literature. These techniques ran
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