Exploring the tools and methods to evaluate influence of social groups on individual occupant behavior with impact on energy use
Author(s) -
Masab Khalid Annaqeeb,
Jakub Dziedzic,
Da Yan,
Vojislav Novaković
Publication year - 2019
Publication title -
iop conference series earth and environmental science
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.179
H-Index - 26
eISSN - 1755-1307
pISSN - 1755-1315
DOI - 10.1088/1755-1315/352/1/012044
Subject(s) - construct (python library) , social group , diversity (politics) , key (lock) , group (periodic table) , energy (signal processing) , relation (database) , computer science , psychology , social relation , social psychology , data science , mathematics , sociology , data mining , computer security , statistics , chemistry , organic chemistry , anthropology , programming language
One of the key elements in driving the energy performance of buildings has been recognized as occupant behavior (OB). However, available tools for assessing and simulating occupant behavior are based on fixed schedules and aggregated profiles, which fail to capture the diversity of OB. A significant aspect of OB is its relation to social groups and their influence and interdependence on each other. The data regarding the influence of social groups is important to achieve an effective model of OB as it accentuates the individual OB profile based on the influences it can have from the social groups they belong to. This added module is not present in traditional building simulation tools. This study aims to explore the tools and methods to evaluate the factors that are responsible for the influence of social groups on individuals’ energy-related behavior. The paper investigates the kind of data sets needed for understanding this interdependence, including the occupant’s social group, their standing in the group, and the intent behind different actions and its comparison to the actions the individual would take without any external influence. The results will be used to construct questionnaires, which can prove beneficial in developing social group profiles in OB models.
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