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Details of Grant 

EPSRC Reference: EP/K002643/1
Title: Digital Agent Networking for Customer Energy Reduction (DANCER)
Principal Investigator: Russo, Professor R
Other Investigators:
Anderson, Dr B Yang, Professor K
Researcher Co-Investigators:
Project Partners:
British Gas Croydon Council
Department: Psychology
Organisation: University of Essex
Scheme: Standard Research - NR1
Starts: 01 August 2012 Ends: 31 July 2017 Value (£): 771,789
EPSRC Research Topic Classifications:
Energy Efficiency Human-Computer Interactions
EPSRC Industrial Sector Classifications:
Related Grants:
Panel History:
Panel DatePanel NameOutcome
15 Mar 2012 TEDDI Announced
Summary on Grant Application Form
Long-term energy consumption reduction can be achieved more readily through sensible cooperation between end users and technological advancements. Monitoring energy use within buildings requires clear and reliable methods with outputs that are meaningful and helpful. End users play a pivotal role in this as energy use revolves around their presence and comfort. Hence, with changing lifestyles and working patterns, energy consumption reduction can be aided by new approaches in digital innovation. Energy metering schemes are now popular and provide data on energy use and cost, but communicatively are a one-way street. Hence, this information is only beneficial if users continually make changes to utility use within their home. However, behavioural changes inducing energy reduction fade relatively quickly and users feel less empowered. Last year, residential sector emissions rose by 13.4% despite metering being a popular investment. Based on this information, interactive systems can help address this problem.

Consumers appreciate that innovative technology can increase their quality of life. However, a lasting bond between the two can only occur when users have confidence in the technology around them. This is more likely to happen when users and technologists work collectively in the system design process. DANCER takes insights from users' behaviour analysis, metering schemes, wireless sensors and embedded software to produce a system that both interactively and automatically manages users' energy consumption within indoor environments. It will tailor users' energy consumption to their habits aiming to reduce energy consumption. To achieve this DANCER adopts a multidisciplinary approach where knowledge from psychology, social and economic research, wireless communication and computer science unite to provide a viable solution that is beneficial to all the stakeholders on the energy supply-consumer chain.

To the above aim, users' energy consumption habits will be collected and studied to inform both the design of the energy control system as well as the user interface in the DANCER system. Baseline information will be collected from samples of end users. This will be combined with insights from the relevant emerging literature. Moreover, an iterative participatory design approach will be used to explore how users feel about the digital technologies to be employed in this project and how they imagine these technologies can assist them in reducing their energy consumption and carbon footprint. Increasingly mature versions of the DANCER system will then be pre-tested through a series of pilot studies with volunteers so that users' queries about the sensors, networks and control policies being used to monitor and interactively manage their energy use can be further examined. Finally, the mature DANCER system will be tested in a control trial experiment where samples of households will be either provided with DANCER or allocated to appropriate control conditions. The trial will enable the analysis of the effect of the system on users' energy related behaviours, energy and carbon emissions.

The DANCER system will act as follows. Wireless sensor networks will employ novel sensing and communication mechanisms which will monitor users' movements and the energy use of a range of appliances. These data, together with the information either collected directly from end users via their smart phone application (e.g. indications to reduce energy use by 20%) or inferred indirectly from user habits, will be fed into a decision making agent that will decide when to switch on/off certain appliances and for how long. The above information collected by the agent, on a per-dwelling basis, will be sent to a centralised remote database. As a result, a global view of the energy consumption and user habits can be derived. In return, this information can be used to guide stakeholders to more effective and efficient way of supplying and consuming energy.
Key Findings
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Potential use in non-academic contexts
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Date Materialised
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