Assessment of end-use electricity consumption and peak demand by Townsville's housing stock

We have developed a comprehensive model to estimate annual end-use electricity consumption and peak demand of housing stock, considering occupants' use of air conditioning systems and major appliances. The model was applied to analyse private dwellings in Townsville, Australia's largest tropical city. For the financial year (FY) 2010–11 the predicted results agreed with the actual electricity consumption with an error less than 10% for cooling thermostat settings at the standard setting temperature of 26.5°C and at 1.0°C higher than the standard setting. The greatest difference in monthly electricity consumption in the summer season between the model and the actual data decreased from 21% to 2% when the thermostat setting was changed from 26.5°C to 27.5°C. Our findings also showed that installation of solar panels in Townville houses could reduce electricity demand from the grid and would have a minor impact on the yearly peak demand. A key new feature of the model is that it can be used to predict probability distribution of energy demand considering (a) that appliances may be used randomly and (b) the way people use thermostats. The peak demand for the FY estimated from the probability distribution tracked the actual peak demand at 97% confidence level.

[1]  Xiaoming Wang,et al.  Assessment of climate change impact on residential building heating and cooling energy requirement in Australia , 2010 .

[2]  Corinne Le Quéré,et al.  Climate Change 2013: The Physical Science Basis , 2013 .

[3]  W. Beckman,et al.  SOLAR ENGINEERING OF THERMAL PROCESSES Second Edition , 2009 .

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

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

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

[7]  Dirk P. Kroese,et al.  Handbook of Monte Carlo Methods , 2011 .

[8]  Zhengen Ren,et al.  Enhanced air flow modelling for AccuRate – A nationwide house energy rating tool in Australia , 2010 .

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

[10]  Zhengen Ren,et al.  A local-community-level, physically-based model of end-use energy consumption by Australian housing stock , 2012 .

[11]  Zhengen Ren,et al.  AusZEH Design: software for low-emission and zero-emission house design in Australia , 2011 .

[12]  Zhengen Ren,et al.  Climate change adaptation pathways for Australian residential buildings , 2011 .

[13]  Vincent R. Gray.  Climate Change 2007: The Physical Science Basis Summary for Policymakers , 2007 .

[14]  Nando de Freitas,et al.  Sequential Monte Carlo Methods in Practice , 2001, Statistics for Engineering and Information Science.