Appliance daily energy use in new residential buildings: Use profiles and variation in time-of-use

Abstract One of the largest user of electricity in the average U.S. household is appliances, which when aggregated, account for approximately 30% of electricity used in the residential building sector. As influencing the time-of-use of energy becomes increasingly important to control the stress on today's electrical grid infrastructure, understanding when appliances use energy and what causes variation in their use are of great importance. However, there is limited appliance-specific data available to understand their use patterns. This study provides daily energy use profiles of four major household appliances: refrigerator, clothes washer, clothes dryer, and dishwasher, through analyzing disaggregated energy use data collected for 40 single family homes in Austin, TX. The results show that when compared to those assumed in current energy simulation software for residential buildings, the averaged appliance load profiles have similar daily distributions. Refrigerators showed the most constant and consistent use. However, the three user-dependent appliances, appliances which depend on users to initiate use, varied more greatly between houses and by time-of-day. During peak use times, on weekends, and in homes with household members working at home, the daily use profiles of appliances were less consistent.

[1]  Lene Nielsen,et al.  How to get the birds in the bush into your hand: Results from a Danish research project on electricity savings , 1993 .

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

[3]  Stéphane Ploix,et al.  A prediction system for home appliance usage , 2013 .

[4]  Alexis Kwasinski,et al.  Experimental and data collection methods for a large-scale smart grid deployment: Methods and first results , 2014 .

[5]  Vice President,et al.  AMERICAN SOCIETY OF HEATING, REFRIGERATION AND AIR CONDITIONING ENGINEERS INC. , 2007 .

[6]  Paul Wattles,et al.  Load resources providing Ancillary Services in Electric Reliability Council of Texas (ERCOT) , 2012, 2012 IEEE PES Innovative Smart Grid Technologies (ISGT).

[7]  E. E. Richman,et al.  Metered end-use consumption and load shapes from the ELCAP residential sample of existing homes in the Pacific Northwest , 1993 .

[8]  Robert Hendron,et al.  Building America House Simulation Protocols , 2010 .

[9]  M. Newborough,et al.  Dynamic energy-consumption indicators for domestic appliances: environment, behaviour and design , 2003 .

[10]  Craig Christensen,et al.  BEopt(TM) Software for Building Energy Optimization: Features and Capabilities , 2006 .

[11]  Tracey Crosbie,et al.  Household Energy Studies: The Gap between Theory and Method , 2006 .

[12]  Stéphane Ploix,et al.  Prediction of appliances energy use in smart homes , 2012 .

[13]  N. J. Kelly,et al.  The effect of appliance energy efficiency improvements on domestic electric loads in European househ , 2011 .

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

[15]  Ian Beausoleil-Morrison,et al.  Measured end-use electric load profiles for 12 Canadian houses at high temporal resolution , 2012 .

[16]  Lingfeng Wang,et al.  Smart charging and appliance scheduling approaches to demand side management , 2014 .

[17]  R. Dear,et al.  Weather sensitivity in household appliance energy end-use , 2004 .