Smart homes, towards individualised campaigns to manage domestic energy consumption
Lead Research Organisation:
Imperial College London
Department Name: Imperial College Business School
Abstract
In the UK, the domestic sector, excluding transportation, accounts for 11% of the national energy usage (CarbonBrief, 2015) and it saw its share increase by 3.6% from 2015 (BEIS, 2016). On average a 2-person British household pays around £790 in bills for electricity and gas (UKPower, 2017), with an additional £380 for water (TheTelegraph, 2017) per year. While prices of dual fuel deals vary across suppliers, it remains that the average person in the UK consumes both electricity and water excessively. Reducing our home energy and water usage would have a direct impact on national figures, thus participating in the national efforts towards achieving carbon mitigation targets. Among various carbon reduction strategies, the smart meter rollout aims to equip each of the 26 million British households with smart meters by 2020 (DECC, 2013). This will enable government and companies to implement large scale Energy Demand Side Management (EDSM) measures to help consumers reduce their energy and water usage using more appropriate feedback and billing systems than existing ones. This PhD project revolves around a very simple question: how are consumers in the residential sector incentivized to reduce their energy consumption? Existing research in the literature, that will be presented in more detail in the next section, has considered different levels of interventions aiming at triggering behavioral change, still findings are far from being homogeneous across studies since both contexts, methods and achievements vary significantly between experimental setups.
Technological progress now allows personalised intervention: smart meters collect data in real time, smart application dispatch user friendly feedback and machine learning enables precise profiling. Research is needed to understand underlying decision mechanisms which lead consumers to adapt their behaviours in response to feedback mechanisms. Comparing the performance of each behavioural intervention is necessary to identify the best mechanism in the relevant context, alongside with psychological research on discrepancies across individuals towards designing personalised interventions which suit each consumer.
Still if these reductions cannot be stretched any further, what solutions are left to control domestic appliances and better manage consumers' assets? While overloading consumers with information exhausts their interest in campaigns (Pugh, 2016), technologies which avoid consumers to even think of their consumption by managing it on their behalf are expected to hatch and quickly develop in British homes (SmartEnergy GB, 2016).
This work could help tailor feedback messages to individuals according to predictive algorithms based on near-real time data collection. To do so, the intervention would identify and exploit the mechanism which is the most likely to succeed in sustaining reductions in energy and water consumption. This should provide evidence that feedback intervention best shines when coupled with systems which support consumers in managing their appliances. From a research perspective, given a certain home configuration this work should provide a deeper understanding of the psychology of energy consumption and help towards identifying which feedback mechanism best fits the targeted consumer. The results of this research could be of relevance, and therefore impact both policy makers, because it will give the understanding of how to achieve efficient reductions needed for society, and utility companies, because although they do not particularly want reductions in consumption, a better understanding and service to their customers could lead to customer retention (important when competition) and more efficient utilisation and control of their system and resources.
Technological progress now allows personalised intervention: smart meters collect data in real time, smart application dispatch user friendly feedback and machine learning enables precise profiling. Research is needed to understand underlying decision mechanisms which lead consumers to adapt their behaviours in response to feedback mechanisms. Comparing the performance of each behavioural intervention is necessary to identify the best mechanism in the relevant context, alongside with psychological research on discrepancies across individuals towards designing personalised interventions which suit each consumer.
Still if these reductions cannot be stretched any further, what solutions are left to control domestic appliances and better manage consumers' assets? While overloading consumers with information exhausts their interest in campaigns (Pugh, 2016), technologies which avoid consumers to even think of their consumption by managing it on their behalf are expected to hatch and quickly develop in British homes (SmartEnergy GB, 2016).
This work could help tailor feedback messages to individuals according to predictive algorithms based on near-real time data collection. To do so, the intervention would identify and exploit the mechanism which is the most likely to succeed in sustaining reductions in energy and water consumption. This should provide evidence that feedback intervention best shines when coupled with systems which support consumers in managing their appliances. From a research perspective, given a certain home configuration this work should provide a deeper understanding of the psychology of energy consumption and help towards identifying which feedback mechanism best fits the targeted consumer. The results of this research could be of relevance, and therefore impact both policy makers, because it will give the understanding of how to achieve efficient reductions needed for society, and utility companies, because although they do not particularly want reductions in consumption, a better understanding and service to their customers could lead to customer retention (important when competition) and more efficient utilisation and control of their system and resources.
Organisations
People |
ORCID iD |
| Mirabelle Muuls (Primary Supervisor) | |
| Quentin Coutellier (Student) |
Studentship Projects
| Project Reference | Relationship | Related To | Start | End | Student Name |
|---|---|---|---|---|---|
| EP/N509486/1 | 30/09/2016 | 30/03/2022 | |||
| 1803446 | Studentship | EP/N509486/1 | 30/09/2016 | 30/03/2020 | Quentin Coutellier |
| Description | 1st project: We report first results from a large-scale randomized controlled trial of various forms of energy consumption feedback facilitated by smart meters and smart phone apps. Nearly 40,000 customers of a large energy retailer in the UK were exposed to either basic feedback apps---i.e. simply giving consumers access to accurate monthly energy consumption information---or more advanced feedback involving peer group comparisons as well as disaggregation of total electricity consumption. In our intent-to-treat analysis, we find that more advanced feedback can lead to an average consumption reduction of nearly 4\%. Focusing solely on individuals who comply with treatment by signing in suggests the reduction is up to 12\%. 2nd project: Furthermore, while it is assumed that the mechanism by which descriptive norms work to change behaviour is by motivating individuals to align their behaviour (consumption) with that of their peers, this has never been truly tested and confirmed. An alternative explanation for this effect is simply that the descriptive norm acts as a reference point to highlight the feedback-standard gap. If this is the case, this would mean that people would equally benefit from any form of feedback that helps in benchmarking their personal consumption. The current series of studies therefore seeks to determine whether descriptive norms change behaviour through social motivation or simply through acting as a reference point for consumption, and if the latter is true, whether there is a difference between the different forms of comparison feedback interventions. To test this first aspect, a framed field experiment will be conducted in a student resident building where water, energy and heating consumption are measured at an individual room level. Participants will be randomly assigned to either a social or prescriptive norm incongruous or congruous group. The incongruous group will receive genuine descriptive norms regarding their consumption coupled with a prescriptive norm that is counter to it. For example, an individual receiving feedback that their consumption is similar to the average consumption of their peers will also receive prescriptive feedback that will suggest that their consumption is more than the objectively "optimal" consumption. Alternatively, an individual receiving feedback that their consumption is more than the average of their peers will receive prescriptive feedback that suggests that their consumption is objectively ideal. Those in the congruous group will therefore receive prescriptive feedback that is congruous with the descriptive feedback. 3rd project: Demand response strategies that rely on individual behavior change have consistently demonstrated that flexibility in energy demand exists, but that there are constraints to behavioral demand shifting such as limited attention. This research aims to introduce residential energy demand flexibility using WiFi-enabled smart plugs, largely bypassing the need for home dwellers' attention and effort altogether. In these field trials comprising 150 subjects, we study the adoption and user interaction of devices for automated dynamic energy demand management. The trials allow us to explore whether and how devices and incentive systems may be designed and deployed to improve load balancing in the centralized energy grid. Such a system would reduce the need for inefficient and often carbon-intensive back-up power generators during periods of peak demand, and increase the possible share of energy supplied by intermittent renewable energy sources. |
| Exploitation Route | Outcomes of 1st project:These effects are concentrated among customers served by a particular installation firm, which displays higher capabilities along a number of metrics, suggesting an important role for firms installing smart technologies targeting energy conservation and flexibility. In the UK, smart meters are by default installed with in-home displays (IHDs) that provide real-time feedback on energy use. Some of the customers in our sample did not receive an IHD and we explore if this endogenous intervention had any impact on the effect described above. Cautioning against drawing causal conclusions, we do \emph{not} find any evidence that energy reduction is contingent on IHDs. Outcomes of 2nd project: Descriptive social norms are presented by utility companies to consumers around the world, with the goal of reducing their consumption. This has been predominantly due to the positive results of this intervention found in several high profile large scale randomised control trials in recent years. Despite the success of these studies, a recent meta-analysis comparing different forms of comparison feedback interventions suggests that social norm comparison may not be the most effective feedback when compared to alternative feedback interventions such as historical consumption comparison or goal comparisons. Outcomes of 3rd project: The proposed project provides valuable insights into the decision-making process regarding energy consumption and IoT applications in this context. The results bridge the more quantitative research tradition of rational-choice modelling (as employed, for instance, in economics) with the more qualitative research tradition of behavioural decision-making (as employed, for instance, in psychology). The outcomes are thus likely to be valuable for researchers in both camps. |
| Sectors | Energy |