ENLITEN household dynamic study datasets [for paper 'Knowing your family: The accuracy of proxy reports of household environmental values, attitudes and behaviours in relation to energy saving']

Two data sets from two survey based studies which examine behavioural antecedent variables in household groups. The documentation files describe the variable in the data-set. The Methodology and Materials documents describe the procedure and the surveys themselves are also attached.Surveys given to household groups. See methodology and materials documents for further details

ENLITEN card sorting data [for the paper "Householders' Mental Models of Domestic Energy Consumption: Using a Sort-and- Cluster Method to Identify Shared Concepts of Appliance Similarity"]

Two data sets containing the results of two card sorting tasks which aim to reveal mental models of home energy consumption and food items, alongside data sets which detail the participant’s demographic information is stored. Details of the data sets can be found in the card sort documentation file. The data has been written up as a paper and the manuscript submitted to PLOSone is attached. Further details of the nature of the study can be found in the Instructions and methodology document, details of the briefing, consent and debriefing procedure can be found in the materials documents for each task.See attached PDF documents for details of methodolog

ENLITEN focus group data

A series of 5 focus groups and one interview were undertaken in April 2013 in Exeter UK. The participants were tenants and leaseholders of Exeter City Council (ECC) social housing. Participants were recruited by the resident involvement officer at ECC. The focus groups involved three tasks: 1. A card sort task (see associated card sort dataset record) 2. A discussion about energy consumption in the home 3. A branding exercise where participants were told about plans to run a large monitoring study (see ENLITEN project databases) asked their opinions on various potential aspects of the study. Each focus group was recorded and the audio files were edited so that all relevant information to tasks 1 & 2 (psy1-6), and 3 (rec 1-6) were separated. The information relevant to tasks 1 & 2 was transcribed, however the information relevant to task 3 was thematically analysed directly from the audio files and a draft internal report was created contained this data (ENLITEN branding focus group results). One session was an interview as only one participant turned up. Further details about the focus groups can be found in the focus group materials and interview schedules document attached.Focus groups took place at a community center in Exeter in April 2013. The sessions were audio recorded. For further details see interview schedule and materials

Energy behaviour change model validation

This dataset includes psychological data (environmental values, success expectancy, perceived barriers for pro-environmental behaviour, general energy literacy) and electricity consumption data for 20 households in Exeter, UK. This dataset was created within the ENLITEN project funded by EPSRC (grant number EP/K002724/1)The conceptual model described in the paper, “A Cognitive Agent-Based Model for Energy Behaviour Change Interventions” (Mogles, N., Padget, J., Gabe-Thomas, E., Walker, I., Lee, J.) was implemented in the MATLAB environment. The file containing the code for running model simulations is called energy_beh_change_model.m and it was run using MATLAB version MATLAB_R2014b

Designing sensor sets for capturing energy events in buildings

There is a growing desire to measure the operational performance of buildings – often many buildings simultaneously – but the cost of sensors and complexity of deployment is a significant constraint. In this paper, we present an approach to minimising the cost of sensing by recognising that researchers are often not interested in the raw data itself but rather some inferred performance metric (e.g. high CO2 levels may indicate poor ventilation). We cast the problem as one of constrained optimisation – specifically, as a bounded knapsack problem (BKP) – to choose the best sensors for the set given each sensor's predictive value and cost. Training data is obtained from a field study comprising a wide range of possible sensors from which a minimum set can be extracted. We validate the method using reliable self-reported event diaries as a measure of actual performance. Results show that the method produces sensors sets that are good predictors of performance and the optimal sets vary substantially with the constraint parameters. Furthermore, valuable yet expensive sensors are often not chosen in the optimal set due to strong co-incidence of sensor signals. For example, light level and sound level often increase at the same time. The overall implication of the work is that a large number of co-incident low-cost sensors can be used to build up a picture of building performance, without significantly compromising information content, and this could have major benefits for the smart metering industry

The reliability of inverse modelling for the wide scale characterization of the thermal properties of buildings

The reduction of energy use in buildings is a major component of greenhouse gas mitigation policy and requires knowledge of the fabric and the occupant behaviour. Hence there has been a longstanding desire to use automatic means to identify these. Smart metres and the internet-of-things have the potential to do this. This paper describes a study where the ability of inverse modelling to identify building parameters is evaluated for 6 monitored real and 1000 simulated buildings. It was found that low-order models provide good estimates of heat transfer coefficients and internal temperatures if heating, electricity use and CO2 concentration are measured during the winter period. This implies that the method could be used with a small number of cheap sensors and enable the accurate assessment of buildings’ thermal properties, and therefore the impact of any suggested retrofit. This has the potential to be transformative for the energy efficiency industry.</p

Overheating in vulnerable and non-vulnerable households

As the 2003 European heatwave demonstrated, overheating in homes can cause wide-scale fatalities. With temperatures and heatwave frequency predicted to increase due to climate change, such events can be expected to become more common. Thus, investigating the risk of overheating in buildings is key to understanding the scale of the problem and in designing solutions. Most work on this topic has been theoretical and based on lightweight dwellings that might be expected to overheat. By contrast, this study collects temperature and air quality data over two years for vulnerable and non-vulnerable UK homes where overheating would not be expected to be common. Overheating was found to occur, particularly and disproportionately in households with vulnerable occupants. As the summers in question were not extreme and contained no prolonged heatwaves, this is a significant and worrying finding. The vulnerable homes were also found to have worse indoor air quality. This suggests that some of the problem might be solved by enhancing indoor ventilation. The collected thermal comfort survey data were also validated against the European adaptive model. Results suggest that the model underestimates discomfort in warm conditions, having implications for both vulnerable and non-vulnerable homes