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

This paper presents a new approach to bottom-up stochastic occupant behaviour modelling for predicting the use of household electrical appliances in domestic buildings. Three metrics relating to appliance occupant behaviours are defined: the number of switch-on events per day, the switch-on times and the duration of each appliance usage. The metrics were calculated for 1,076 appliances in 225 households from the UK Government’s Household Electricity Survey carried out in 2010–2011. The analysis shows that occupant behaviour varies substantially between households, across appliance types and over time. The new modelling approach improves on previous approaches by using a three-step process where the three-appliance occupant-behaviour metrics are simulated respectively using stochastic processes to capture daily variations in appliance occupant behaviour. It uses probability and cumulative density functions based on individual households and appliances which are shown to have advantages for modelling the variations in appliance occupant behaviours.

[1]  David E. Claridge.  Accounting for the Occupancy Variable in Inverse Building Energy Baselining Models , 2001 .

[2]  Jie Zhao,et al.  Occupant behavior and schedule modeling for building energy simulation through office appliance power consumption data mining , 2014 .

[3]  Tianzhen Hong,et al.  Simulation of occupancy in buildings , 2015 .

[4]  Darren Robinson,et al.  A bottom-up stochastic model to predict building occupants' time-dependent activities , 2013 .

[5]  Ardeshir Mahdavi,et al.  IEA EBC Annex 66: Definition and simulation of occupant behavior in buildings , 2017 .

[6]  Aya Hagishima,et al.  A methodology for peak energy requirement considering actual variation of occupants' behavior schedules , 2008 .

[7]  Yoshiyuki Shimoda,et al.  OCCUPANT BEHAVIOR MODEL FOR HOUSEHOLDS TO ESTIMATE HIGH- TEMPORAL RESOLUTION RESIDENTIAL ELECTRICITY DEMAND PROFILE , 2011 .

[8]  Fredrik Karlsson,et al.  Indoor climate in low-energy houses : an interdisciplinary investigation , 2006 .

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

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

[11]  V. Ismet Ugursal,et al.  Modeling of end-use energy consumption in the residential sector: A review of modeling techniques , 2009 .

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

[13]  K. Steemers,et al.  A method of formulating energy load profile for domestic buildings in the UK , 2005 .

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

[15]  A. Grandjean,et al.  A review and an analysis of the residential electric load curve models , 2012 .

[16]  Parliamentary Counsel.  Department of Energy and Climate Change (Abolition) Bill , 2015 .

[17]  Kevin J. Lomas,et al.  Identifying trends in the use of domestic appliances from household electricity consumption measurements , 2008 .

[18]  Jessen Page,et al.  Simulating occupant presence and behaviour in buildings , 2007 .

[19]  Jukka Paatero,et al.  A model for generating household electricity load profiles , 2006 .

[20]  David Infield,et al.  Domestic electricity use: A high-resolution energy demand model , 2010 .

[21]  Kajsa Ellegård,et al.  Models of domestic occupancy, activities and energy use based on time-use data: deterministic and stochastic approaches with application to various building-related simulations , 2012 .

[22]  Lieve Helsen,et al.  The Impact of Load Profile on the Grid-Interaction of Building Integrated Photovoltaic (BIPV) Systems in Low-Energy Dwellings , 2010 .

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

[24]  John Mardaljevic,et al.  Dynamic Daylight Performance Metrics for Sustainable Building Design , 2006 .

[25]  Regina Lamedica,et al.  A bottom-up approach to residential load modeling , 1994 .

[26]  Aya Hagishima,et al.  Total utility demand prediction system for dwellings based on stochastic processes of actual inhabitants , 2010 .

[27]  P Pieter-Jan Hoes,et al.  User behavior in whole building simulation , 2009 .

[28]  Björn Karlsson,et al.  End User Value of On-Site Domestic Photovoltaic Generation with Different Metering Options in Sweden , 2010 .

[29]  Nick Baker,et al.  Natural ventilation in practice: linking facade design, thermal performance, occupant perception and control , 2008 .

[30]  Darren Robinson,et al.  Verification of stochastic models of window opening behaviour for residential buildings , 2012 .