Experimental Data Acquisition

The acquisition of people’s behaviours in buildings is extremely important for the evaluation and the improvement of the building performances, as well as for the development of behavioural predictive models. Both occupancy patterns and users’ interactions with devices influence the building energy use. Therefore, researchers adopted many techniques to record these data, customising the monitoring system according to the building features and the research aim. This Chapter offers an overview of the techniques adopted in experimental campaigns to detect occupancy patterns and acquire behavioural and environmental data.

[1]  D. Linton,et al.  Occupancy Monitoring Using Passive RFID Technology for Efficient Building Lighting Control , 2012, 2012 Fourth International EURASIP Workshop on RFID Technology.

[2]  Dj Carter,et al.  Long-term patterns of use of occupant controlled office lighting , 2003 .

[3]  Christoph F. Reinhart,et al.  Monitoring manual control of electric lighting and blinds , 2003 .

[4]  Edward Arens,et al.  Opportunities to save energy and improve comfort by using wireless sensor networks in buildings , 2003 .

[5]  Darren Robinson,et al.  On the behaviour and adaptation of office occupants , 2008 .

[6]  Gian Marco Revel,et al.  A Low-Cost Sensor for Real-Time Monitoring of Indoor Thermal Comfort for Ambient Assisted Living , 2014 .

[7]  Mehlika Inanici,et al.  A Study of Luminance Distribution Patterns and Occupant Preference in Daylit Offices , 2009 .

[8]  Arno Schlueter,et al.  Occupant centered lighting control for comfort and energy efficient building operation , 2015 .

[9]  Vítor Leal,et al.  Occupants interaction with electric lighting and shading systems in real single-occupied offices: Results from a monitoring campaign , 2013 .

[10]  Michael A. Humphreys,et al.  Updating the adaptive relation between climate and comfort indoors; new insights and an extended database , 2013 .

[11]  Aya Hagishima,et al.  State transition probability for the Markov Model dealing with on/off cooling schedule in dwellings , 2005 .

[12]  James Woods.  Fiddling with Thermostats: Energy Implications of Heating and Cooling Set Point Behavior , 2006 .

[13]  Yi Jiang,et al.  Influence of household air-conditioning use modes on the energy performance of residential district cooling systems , 2016 .

[14]  Sheikh Tahir Bakhsh,et al.  Indoor positioning in Bluetooth networks using fingerprinting and lateration approach , 2011, 2011 International Conference on Information Science and Applications.

[15]  Andreas K. Athienitis,et al.  Manually-operated window shade patterns in office buildings: A critical review , 2013 .

[16]  Ian Beausoleil-Morrison,et al.  A critical review of observation studies, modeling, and simulation of adaptive occupant behaviors in offices , 2013 .

[17]  Therese Peffer,et al.  How people use thermostats in homes: A review , 2011, Building and Environment.

[18]  Zhenghua Chen,et al.  Comparing occupancy models and data mining approaches for regular occupancy prediction in commercial buildings , 2017 .

[19]  Sami Karjalainen,et al.  Thermal comfort and use of thermostats in Finnish homes and offices , 2009 .

[20]  Nabil Nassif.  CO2-BASED DEMAND-CONTROLLED VENTILATION CONTROL STRATEGIES FOR MULTI-ZONE HVAC SYSTEMS , 2011 .

[21]  Patrick James,et al.  Naturally ventilated classrooms: An assessment of existing comfort models for predicting the thermal sensation and preference of primary school children , 2012 .

[22]  Tadj Oreszczyn,et al.  Occupant control of passive systems: the use of Venetian blinds , 2001 .

[23]  Tianzhen Hong,et al.  A data-mining approach to discover patterns of window opening and closing behavior in offices , 2014 .

[24]  Belinda L Collins,et al.  Window blinds as a potential energy saver: a case study. Building science series (final). [Effects of building orientation] , 1978 .

[25]  S. Iliffe,et al.  Bmc Medical Research Methodology Open Access the Hawthorne Effect: a Randomised, Controlled Trial , 2022 .

[26]  Jian Yao,et al.  Determining the energy performance of manually controlled solar shades: A stochastic model based co-simulation analysis , 2014 .

[27]  J. F. Nicol.  Characterising occupant behaviour in buildings : towards a stochastic model of occupant use of windows, lights, blinds, heaters and fans , 2001 .

[28]  J. F. Nicol,et al.  Thermal comfort: use of controls in naturally ventilated buildings , 2001 .

[29]  Chungyoon Chun,et al.  Research on seasonal indoor thermal environment and residents' control behavior of cooling and heating systems in Korea , 2009 .

[30]  Henk Visscher,et al.  The effect of occupancy and building characteristics on energy use for space and water heating in Dutch residential stock , 2009 .

[31]  Mpj Mariëlle Aarts,et al.  Building automation and perceived control : a field study on motorized exterior blinds in Dutch offices , 2014 .

[32]  Guang-Zhong Yang,et al.  Behaviour Profiling with Ambient and Wearable Sensing , 2007, BSN.

[33]  Steven K. Firth,et al.  Smart homes, control and energy management: how do smart home technologies influence control over energy use and domestic life? , 2015 .

[34]  Xuemei Guo,et al.  Compressive classification of human motion using pyroelectric infrared sensors , 2014, Pattern Recognit. Lett..

[35]  P Pieter-Jan Hoes,et al.  On occupant-centric building performance metrics , 2017 .

[36]  Richard de Dear,et al.  Weather, clothing and thermal adaptation to indoor climate , 2003 .

[37]  Josefin Voigt.  Angular positioning of a door or window - using a MEMS accelerometer and a magnetometer , 2015 .

[38]  M S. Rea,et al.  Window blind occlusion: a pilot study , 1984 .

[39]  Belinda L Collins,et al.  Window blinds as a potential energy saver , 1978 .

[40]  Mehlika Inanici,et al.  The Effect of Luminance Distribution Patterns on Occupant Preference in a Daylit Office Environment , 2010 .

[41]  Nabil Nassif,et al.  A robust CO2-based demand-controlled ventilation control strategy for multi-zone HVAC systems , 2012 .

[42]  Vorapat Inkarojrit,et al.  Indoor climatic influences on the operation of windows in a naturally ventilated building , 2004 .

[43]  Marcus M. Keane,et al.  A performance assessment ontology for the environmental and energy management of buildings , 2015 .

[44]  Andreas Wagner,et al.  Does the occupant behavior match the energy concept of the building? - Analysis of a German naturally ventilated office building , 2015 .

[45]  Stefano Schiavon,et al.  Dynamic predictive clothing insulation models based on outdoor air and indoor operative temperatures , 2013 .

[46]  Francesca Stazi,et al.  A literature review on driving factors and contextual events influencing occupants' behaviours in buildings , 2017 .

[47]  Chuang Wang,et al.  Air-conditioning usage conditional probability model for residential buildings , 2014 .

[48]  Alberto Giretti,et al.  Stochastic behavioural models of occupants' main bedroom window operation for UK residential buildings , 2017 .

[49]  Astrid Roetzel,et al.  A review of occupant control on natural ventilation , 2010 .

[50]  H. Burak Gunay,et al.  The contextual factors contributing to occupants' adaptive comfort behaviors in offices – A review and proposed modeling framework , 2014 .

[51]  Richard E. Brown,et al.  After-hours power status of office equipment in the USA , 2005 .

[52]  Francesca Stazi,et al.  Indoor air quality and thermal comfort optimization in classrooms developing an automatic system for windows opening and closing , 2017 .

[53]  Valentina Fabi,et al.  Effect of thermostat and window opening occupant behavior models on energy use in homes , 2014 .

[54]  Weiming Shen,et al.  Leveraging existing occupancy-related data for optimal control of commercial office buildings: A review , 2017, Adv. Eng. Informatics.

[55]  Anna Laura Pisello,et al.  How peers’ personal attitudes affect indoor microclimate and energy need in an institutional building: Results from a continuous monitoring campaign in summer and winter conditions , 2016 .

[56]  Francesca Stazi,et al.  Modelling window status in school classrooms. Results from a case study in Italy , 2017 .

[57]  Rui Zhang,et al.  Occupancy detection through an extensive environmental sensor network in an open-plan office building , 2009 .

[58]  Ian Beausoleil-Morrison,et al.  On the behavioral effects of residential electricity submetering in a heating season , 2014 .

[59]  Ian Beausoleil-Morrison,et al.  Development and implementation of an adaptive lighting and blinds control algorithm , 2017 .

[60]  Yufan Zhang,et al.  Factors influencing the occupants’ window opening behaviour in a naturally ventilated office building , 2012 .

[61]  Hsm Helianthe Kort,et al.  Occupancy-based lighting control in open-plan office spaces: A state-of-the-art review , 2017 .

[62]  Bjarne W. Olesen,et al.  Occupants' window opening behaviour: A literature review of factors influencing occupant behaviour and models , 2012 .

[63]  Yacine Rezgui,et al.  Building energy metering and environmental monitoring – A state-of-the-art review and directions for future research , 2016 .

[64]  Xuan Luo,et al.  Performance evaluation of an agent-based occupancy simulation model , 2017 .

[65]  Ilias Bilionis,et al.  A Bayesian modeling approach of human interactions with shading and electric lighting systems in private offices , 2017 .

[66]  O. T. Masoso,et al.  The dark side of occupants’ behaviour on building energy use , 2010 .

[67]  Marc Fontoynont,et al.  The use of shading systems in VDU task offices: A pilot study , 2006 .

[68]  Olivia Guerra-Santin,et al.  In-use monitoring of buildings: An overview of data collection methods , 2015 .

[69]  Yi Wang,et al.  Ventilation system type, classroom environmental quality and pupils' perceptions and symptoms , 2014 .

[70]  Peter Barrett,et al.  Factors influencing occupants’ blind-control behaviour in a naturally ventilated office building , 2012 .

[71]  Yang Zhao,et al.  Virtual occupancy sensors for real-time occupancy information in buildings , 2015 .

[72]  P. Gurian,et al.  Tracking the human-building interaction: A longitudinal field study of occupant behavior in air-conditioned offices , 2015 .

[73]  Yoshiyuki Shimoda,et al.  Survey On The Occupant Behavior Relating To Window And Air Conditioner Operation In The Residential Buildings , 2013, Building Simulation Conference Proceedings.

[74]  Ian Richardson,et al.  A high-resolution domestic building occupancy model for energy demand simulations , 2008 .

[75]  Jeong Tai Kim,et al.  Effects of occupancy and lighting use patterns on lighting energy consumption , 2012 .

[76]  Darren Robinson,et al.  Adaptive actions on shading devices in response to local visual stimuli , 2010 .

[77]  K. Steemers,et al.  Time-dependent occupant behaviour models of window control in summer , 2008 .

[78]  Darren Robinson,et al.  Interactions with window openings by office occupants , 2009 .

[79]  Tianzhen Hong,et al.  Occupant behavior modeling for building performance simulation: Current state and future challenges , 2015 .

[80]  Fadi M. Alsaleem,et al.  Sensitivity study for the PMV thermal comfort model and the use of wearable devices biometric data for metabolic rate estimation , 2016 .

[81]  D.R.G. Hunt,et al.  The use of artificial lighting in relation to daylight levels and occupancy , 1979 .

[82]  Burcin Becerik-Gerber,et al.  Performance-based evaluation of RFID-based indoor location sensing solutions for the built environment , 2011, Adv. Eng. Informatics.

[83]  Yingxin Zhu,et al.  Indoor climate and thermal physiological adaptation: Evidences from migrants with different cold indoor exposures , 2016 .

[84]  Rui Zhang,et al.  An information technology enabled sustainability test-bed (ITEST) for occupancy detection through an environmental sensing network , 2010 .

[85]  Joseph Andrew Clarke,et al.  Using results from field surveys to predict the effect of open windows on thermal comfort and energy use in buildings , 2007 .

[86]  Rhys Goldstein,et al.  Real-time occupancy detection using decision trees with multiple sensor types , 2011, SpringSim.

[87]  Ardeshir Mahdavi,et al.  Prediction of plug loads in office buildings: Simplified and probabilistic methods , 2016 .

[88]  Patrick James,et al.  Camera-based window-opening estimation in a naturally ventilated office , 2018 .

[89]  Mario Berges,et al.  Unsupervised disaggregation of appliances using aggregated consumption data , 2011 .

[90]  Tianzhen Hong,et al.  Ten questions concerning occupant behavior in buildings: The big picture , 2017 .

[91]  Li Shao,et al.  Understanding occupancy pattern and improving building energy efficiency through Wi-Fi based indoor positioning , 2017 .

[92]  William O'Brien,et al.  Review of current methods, opportunities, and challenges for in-situ monitoring to support occupant modelling in office spaces , 2017 .