A data-mining approach to discover patterns of window opening and closing behavior in offices

Abstract

Understanding the relationship between occupant behaviors and building energy consumption is one of the most effective ways to bridge the gap between predicted and actual energy consumption in buildings. However effective methodologies to remove the impact of other variables on building energy consumption and isolate the leverage of the human factor precisely are still poorly investigated. Moreover, the effectiveness of statistical and data mining approaches in finding meaningful correlations in data is largely undiscussed in literature. This study develops a framework combining statistical analysis with two data-mining techniques, cluster analysis and association rules mining, to identify valid window operational patterns in measured data. Analyses are performed on a data set with measured indoor and outdoor physical parameters and human interaction with operable windows in 16 offices. Logistic regression was first used to identify factors influencing window opening and closing behavior. Clustering procedures were employed to obtain distinct behavioral patterns, including motivational, opening duration, interactivity and window position patterns. Finally the clustered patterns constituted a base for association rules segmenting the window opening behaviors into two archetypal office user profiles for which different natural ventilation strategies as well as robust building design recommendations that may be appropriate. Moreover, discerned working user profiles represent more accurate input to building energy modeling programs, to investigate the impacts of typical window opening behavior scenarios on energy use, thermal comfort and productivity in office buildings.

Occupant behaviour modelling in domestic buildings: the case of household electrical appliances

“…Studies which model the occupants' interaction with buildings and control systems have included lighting controls (Reinhart et al 2006), shading devices (Haldi and Robinson 2010) and ventilation (Yun et al 2008). Other studies have focused on occupancy presence, fundamental for occupancy research as most occupant behaviour patterns are influenced by occupancy (Roetzel et al 2010;D'Oca and Hong 2014;Zhao et al 2014;Feng et al 2015). Characterising the stochastic nature *Corresponding author.…”

An application of data mining in district heating substations for improving energy performance

“…In the past several decades, it has been successfully applied in economics, retails, telecommunication, and financial services [4]. Recently, efforts have also been made to investigate the application of data mining in HVAC field, including building energy consumption prediction [5,6], building energy management [7,8], fault detection and diagnosis [9,10], and occupant behaviour [11,12].…”

A simulation approach to estimate energy savings potential of occupant behavior measures

“…Field-measured data and large-scale surveys confirmed that these window opening behaviors, which are represented as probabilistic models (logit or Weibull functions), have been adopted by several BPS programs to determine when occupants open windows [32] [33]. Occupant behavior stochastic models are data driven and improve modeling assumptions of occupant activities in the BPS programs [34] [35][36] [37].…”