Study Vision
Reducing energy demand from existing dwellings through occupant behaviour change is crucial to meet UK 2050 carbon emission reduction targets. Dwellings account for 32% of UK energy consumption, and corresponding emissions. A focus on existing dwellings is essential: 80% of the UK 2050 stock already exists. Heat is key, accounting for 80% of domestic demand. Attention to behaviour change is important: behaviour is estimated by DECC to account for 60% of the variance in demand. Using an interdisciplinary sociotechnical conceptual framework, our team of computer scientists, building engineers and sociologists, all based in Edinburgh, will work together to explore the dynamic roles, interactions and boundaries of energy technologies and householder behaviours. For the first time household energy demand will be able to be analysed in great detail across a large number of homes and the effect of behavioural feedback evaluated over a multi-year period.
Previous work in behaviour change for domestic demand reduction through digital means focussed mainly on electricity (often excluding heating) and supplied feedback on overall energy consumption and normative comparisons with other households. Overall consumption feedback creates a loop in which the householder interprets consumption graphs to determine which household practices waste energy, then changes her behaviour to reduce that waste.
Although such feedback can reduce demand, consumption information fails to meet an important precondition of successful behavioural change: it does not explicitly identify the specific behaviours that are wasting energy, but leaves it up to the householder to infer these. Successful change requires recipients of the feedback to realise there is a problem, understand how their behaviour is related to it, and become aware that they can influence it. The CHARM project found that householders used hourly consumption charts to identify the behaviours responsible for the greatest fluctuation in consumption. In another study of how householders interact with consumption feedback from smart meters, it was found that householders used the monitors to detect specific events, e.g., when a computer had been left on. In contrast, this project will focus on examining the effect of providing personalised behavioural feedback to occupants: explicitly identifying the behaviours (e.g., showering and bathing, room heating, major appliance use) that account for energy use/expenditure; and how these could be reduced.
We will create the following Intelligent Domestic Energy Advice Loop (hence the acronym IDEAL): a) detailed in-home sensing, sufficient to b) infer specific demand-related behaviours, enabling c) timely personalised behavioural feedback. We hypothesise that this loop can be tuned to improve reduction in energy demand from dwellings compared to the state-of-the-art consumption feedback methods, especially where behaviours are not easily identified from consumption feedback by non-experts (heating, cooling, etc). An exciting aspect of this loop over the length of the study is that we will be able to modify the feedback we give householders, using the behaviour changes that we observe and infer as due to our feedback-we will be getting feedback about our feedback!
Previous work in behaviour change for domestic demand reduction through digital means focussed mainly on electricity (often excluding heating) and supplied feedback on overall energy consumption and normative comparisons with other households. Overall consumption feedback creates a loop in which the householder interprets consumption graphs to determine which household practices waste energy, then changes her behaviour to reduce that waste.
Although such feedback can reduce demand, consumption information fails to meet an important precondition of successful behavioural change: it does not explicitly identify the specific behaviours that are wasting energy, but leaves it up to the householder to infer these. Successful change requires recipients of the feedback to realise there is a problem, understand how their behaviour is related to it, and become aware that they can influence it. The CHARM project found that householders used hourly consumption charts to identify the behaviours responsible for the greatest fluctuation in consumption. In another study of how householders interact with consumption feedback from smart meters, it was found that householders used the monitors to detect specific events, e.g., when a computer had been left on. In contrast, this project will focus on examining the effect of providing personalised behavioural feedback to occupants: explicitly identifying the behaviours (e.g., showering and bathing, room heating, major appliance use) that account for energy use/expenditure; and how these could be reduced.
We will create the following Intelligent Domestic Energy Advice Loop (hence the acronym IDEAL): a) detailed in-home sensing, sufficient to b) infer specific demand-related behaviours, enabling c) timely personalised behavioural feedback. We hypothesise that this loop can be tuned to improve reduction in energy demand from dwellings compared to the state-of-the-art consumption feedback methods, especially where behaviours are not easily identified from consumption feedback by non-experts (heating, cooling, etc). An exciting aspect of this loop over the length of the study is that we will be able to modify the feedback we give householders, using the behaviour changes that we observe and infer as due to our feedback-we will be getting feedback about our feedback!
