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| 1 |
ID:
125595
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| Publication |
2013.
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| Summary/Abstract |
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.
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| 2 |
ID:
115684
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2012.
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| Summary/Abstract |
We developed a physics based bottom-up model to estimate annual housing stock energy consumption at a local community level (Census Collection District-CCD) with an hourly resolution. Total energy consumption, including space heating and cooling, water heating, lighting and other household appliances, was simulated by considering building construction and materials, equipment and appliances, local climates and occupancy patterns. The model was used to analyse energy use by private dwellings in more than five thousand CCDs in the state of New South Wales (NSW), Australia. The predicted results focus on electricity consumption (natural gas and other fuel sources were excluded as the data are not available) and track the actual electricity consumption at CCD level with an error of 9.2% when summed to state level. For NSW and Victoria 2006, the predicted state electricity consumption is close to the published model (within 6%) and statistical data (within 10%). A key feature of the model is that it can be used to predict hourly electricity consumption and peak demand at fine geographic scales, which is important for grid planning and designing local energy efficiency or demand response strategies.
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| 3 |
ID:
115687
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| Publication |
2012.
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| Summary/Abstract |
We developed a physics based bottom-up model to estimate annual housing stock energy consumption at a local community level (Census Collection District-CCD) with an hourly resolution. Total energy consumption, including space heating and cooling, water heating, lighting and other household appliances, was simulated by considering building construction and materials, equipment and appliances, local climates and occupancy patterns. The model was used to analyse energy use by private dwellings in more than five thousand CCDs in the state of New South Wales (NSW), Australia. The predicted results focus on electricity consumption (natural gas and other fuel sources were excluded as the data are not available) and track the actual electricity consumption at CCD level with an error of 9.2% when summed to state level. For NSW and Victoria 2006, the predicted state electricity consumption is close to the published model (within 6%) and statistical data (within 10%). A key feature of the model is that it can be used to predict hourly electricity consumption and peak demand at fine geographic scales, which is important for grid planning and designing local energy efficiency or demand response strategies.
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