A NOVEL METHODOLOGY FOR GENERATING RESIDENTIAL BUILDINGS ELECTRICITY DEMAND PROFILES

This paper presents the initial development of a novel modelling framework of bottom-up stochastic model that is able to generate realistic electricity demand profiles for domestic appliance use that are based on measured data. Three appliances (washing machine, tumble dryer and dishwasher) are used to explain the model development. 100 homes are simulated for a month. The results of the model were analysed to address the key findings and challenges in modelling high-resolution electricity demand from measured data. It is shown that the model realistically reproduces electricity demand profiles for a large number of households.

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

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

[3]  Y. Shimoda,et al.  Residential end-use energy simulation at city scale , 2004 .

[4]  Yoshiyuki Shimoda,et al.  HOUSEHOLD ELECTRICITY SIMULATION: DEMAND, THE ELECTRICITY GENERATION AND DEMAND-SUPPLY CONTROL METHODS , 2011 .

[5]  Badi H. Baltagi,et al.  Comparison of forecast performance for homogeneous, heterogeneous and shrinkage estimators: Some empirical evidence from US electricity and natural-gas consumption , 2002 .

[6]  G. Rizzoni,et al.  A highly resolved modeling technique to simulate residential power demand , 2013 .

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

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

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

[10]  J. Widén,et al.  A high-resolution stochastic model of domestic activity patterns and electricity demand , 2010 .

[11]  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 .

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

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

[14]  A. Wright,et al.  The nature of domestic electricity-loads and effects of time averaging on statistics and on-site generation calculations , 2007 .

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

[16]  C. F. Walker,et al.  Residential Load Shape Modeling Based on Customer Behavior , 1985, IEEE Power Engineering Review.

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

[18]  Wolfgang Ketter,et al.  Demand side management—A simulation of household behavior under variable prices , 2011 .

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