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.
Research on the energy efficiency of the different components of buildings – their shell, built-in appliances, plug-in appliances, floor size and floor plan, as well as position on site – all have contributions to make to amount of energy consumed. When combined with renewable...Read more
A rapid review on green-rated office buildings, and their operational energy use, found that the conclusions of six studies ranged from the certified buildings performing worse, similarly or much better than the non-certified buildings in terms of energy usage intensity. Two...Read more
In response to feedback, high-income households can reduce their energy use to a larger degree than low-income households (17% vs 3% reduction). This and other insights were gained by two rapid reviews into research, both Australian and International, on digital services and...Read more
Growth in peak electricity demand poses considerable challenges for utilities seeking to ensure secure, reliable yet affordable energy provision. A better understanding of the key drivers of residential peak electricity demand could assist in better managing peak demand growth through options including demand-side participation and energy efficiency programs.
Cost effective reduction of electricity demand in residential sector is a significant problem worldwide. Feedback intervention is a hot area that possesses considerable potential for achieving electricity saving. However, how to make feedback intervention more effective deserves to be properly explored. In the smart grid case study described in this paper, 3666 greater Sydney region households are sampled. Among these sampled households 2814 residences were equipped with 3 different types of feedback technologies.