15 Oct 2015

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.

Journal article

The CRC for Low Carbon Living produced a series of snapshots at the end of 2015 highlighting its achievements in enabling a carbon built environment sector, specifically in the areas of:Once in a generation capacity buildingTransforming industry Products and systems for future buildings and citiesEvidence base for policy, planning and design innovations

Other text
18 Oct 2016

Significant interest exists in the potential for electric vehicles (EVs) to be a source of greenhouse gas (GHG) abatement. In order to establish the extent to which EVs will deliver abatement, however, a realistic understanding of the electricity and transport sector GHG emissions impacts arising from different approaches to integrating EVs into the power system is required.

Journal article
15 Aug 2017

This trial of smart home control in 46 households shows limits to use for managing home energy bills and electricity demand.

Report
16 Feb 2017

    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.

    Journal article
    18 Aug 2015

    Rapid growth of distributed small scale Photovolatic (PV) systems has increased the need for tools that can undertake reliable real time monitoring of system performance with the capability to detect and diagnose underperformance at the earliest possible stage.

    Report
    16 Nov 2016

    This paper presents a computationally efficient smart home energy management system (SHEMS) using an approximate dynamic programming (ADP) approach with temporal difference learning for scheduling distributed energy resources.

    Journal article