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RP1013: Journal article: Statistical analysis of driving factors of residential energy demand in the greater Sydney region, Australia

22015 Fan, H., MacGill, I.F., and Sproul, A.B.: 'Statistical analysis of driving factors of residential energy demand in the greater Sydney region, Australia' in Energy and Buildings, Volume 105, No, 15, (Available online 20 July 2015, published 15 October 2015) pp. 9–25.

Abstract

The residential sector represents some 30% of global electricity consumption but the underlying composition and drivers are still only poorly understood. The drivers are many, varied, and complex, including local climate, household demographics, household behaviour, building stock and the type and number of appliances. There is considerable variation across households and, until recently, often a lack of good data. This study draws upon a detailed household dataset from the Australian Smart Grid Smart City project to build a household electricity consumption model. A statistical linear regression model for household energy demand was established and tested for both individual households and regional aggregations of households. The model showed only reasonable performance in forecasting the consumption of individual households – highlighting the influence of factors beyond those surveyed – but good performance for aggregated household consumption. Models such as this would seem highly useful for a range of stakeholders including individual households trying to understand the potential implications of different choices, utilities looking to better forecast the impact of different possible residential trends and policy makers seeking to assist households in improving their energy efficiency through targeted policies and programs.

Access the full article online at doi:10.1016/j.enbuild.2015.07.030.

View the preprint version: Statistical analysis of driving factors of residential energy demand in the greater Sydney region, Au (1722228 PDF)

Projects: 
RP1013: Enabling Better Utilisation of Distributed Generation with Distributed Storage