SAVING ENERGY IN THE HOME THROUGH DIGITAL

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implications of domestic heating systems with zonal control, identifying the ... In the UK 29% of gas and electricity is used in homes, of which 80% is for space.
SAVING ENERGY IN THE HOME THROUGH DIGITAL FEEDBACK AND CONTROL SYSTEMS: AN INTRODUCTION TO THE DEFACTO PROJECT Becky Mallaband1, Victoria Haines1, Ashley Morton2, Ehab Foda2, Arash Beizaee2 Jacqueline Beckhelling2, David Allinson2, Dennis Loveday2 & Kevin Lomas2 1

Loughborough Design School, Loughborough University, Loughborough, Leicestershire, LE11 3TU, United Kingdom 2 Department of Civil and Building Engineering, Loughborough University, Loughborough, Leicestershire, LE11 3TU, United Kingdom

ABSTRACT There is a substantial need to reduce the amount of energy used to heat domestic properties within the UK. Nearly a third (29%) of the UK gas and electricity consumption is used in homes, of which 80% is for space heating and hot water provision [1]. The DEFACTO project (Digital Energy Feedback And Control Technology Optimisation) is a five year EPSRC-funded project (2012-2017) which is investigating how the use of digital heating control and feedback devices can enable the reduction of this domestic energy use. In particular the project is investigating the implications of domestic heating systems with zonal control, identifying the potential savings incurred. In the UK, digital technology has the potential to provide householders with feedback on their heating energy use and an ability to control where and when this energy is used. However, it is not yet known how much energy these digital technology devices will save and in which households they will work best. This paper introduces the DEFACTO project and reports some of the initial findings which discuss the use of these feedback and control systems within domestic properties. The project has three main phases: an initial planning and testing phase to explore the technology in unoccupied homes and to prepare for in-home trials, followed by two stages of real home studies, one smaller, more detailed pilot study and then a substantial, longitudinal trial involving hundreds of homes. This paper covers findings from the planning and testing phase as well as early findings from the pilot in-home study, but also discusses issues relating to the complexity of conducting the large scale trial.

Context and introduction to the project In the UK 29% of gas and electricity is used in homes, of which 80% is for space heating and hot water provision [1]. Digital technology has the potential to provide households with feedback on their heating energy use and an ability to control where and when energy is used; however, it is unknown how much energy digital technology devices will save and in which households they will work best. The DEFACTO project aims to investigate this area from a number of perspectives through an interdisciplinary approach. The research team takes a number of different perspectives, from the human to the technical aspects of domestic energy demand as well as considering the data management required to organise, maintain and analyse the data collected. The DEFACTO project brings together the domestic 1

energy expertise at Loughborough University in the School of Civil and Building Engineering and the Loughborough Design School. The research focuses on the home as the central focus, using a combination of both an engineering and a user centred approach, necessary for combating complex energy demand problems [2, 3]. The project aims to understand the effectiveness of smart heating controls for reducing energy demand within real homes, to inform the design of future feedback and control devices. Through understanding the way householders use heating energy, it is also anticipated that the errors in energy saving predictions and models can be quantified. The project has three main phases: an initial planning and testing phase to explore the technology in unoccupied homes and to prepare for in-home trials, followed by two stages of real home studies, one smaller, more detailed pilot study and then a substantial, longitudinal trial involving hundreds of homes. This paper covers findings from the planning and testing phase as well as the pilot in-home study, but also discusses issues relating to the complexity of conducting the large scale trial.

Phase 1: The test houses The initial phase of the DEFACTO project was focused around testing the technologies which would later be used in occupied homes. Testing the systems within a controlled environment meant that the possible savings which could be achieved through use of the new controls could be estimated and later compared with any actual savings. In addition, the data made available and the reliability of the monitoring equipment could all be explored in detail without the added complexity of user behaviour. These planning and testing phases were carried out in a pair of identical, adjoining 1930s semi-detached houses in Loughborough (see Figure 1). In order to ascertain the similarity between the houses, a standard blower door test (in accordance with [4]) and a standard co-heating test (as described by [5]) were carried out to evaluate and compare their thermal performance. The results of the characterisation tests displayed very similar airtightness and overall heat transfer coefficients between the two houses. Over an eight week period during winter 2014, the space heating in one of the test houses was controlled conventionally to comply with minimum requirements according to UK Building Regulations Part L1B for existing dwellings [6], whilst in the other house, zonal control was used to heat only the ‘occupied’ rooms. Zonal control enables heating to be controlled and changed depending on which individual rooms are being used at any one time. The main components of these systems are battery operated programmable Thermostatic Radiator Valves (TRVs) which replace the standard TRVs found on many radiators and have motorised valves which control the flow of hot water through the radiators according to a set-point temperature and time schedule. Synthetic occupancy was used to mimic the presence of two working adults and two school aged children, with reference to time use data [7], using equipment such as the examples shown in Figure 2. Thus, heat gains from people, lighting and equipment, internal door opening/closing and window blind operation were applied in an identical manner in both test houses using z-wave enabled devices. This enabled direct comparison between the energy savings of zonal 2

control to conventional control in fully controlled environments, eliminating any influences of inconsistency under each heating control strategy.

Figure 1: Adjoined test houses

Figure 2: An example of synthetic occupancy and monitoring equipment in the living room of one of the test houses

In addition to investigating these potential savings of zonal control versus conventionally controlled heating, the test houses were also used to understand more about the heating controls which would be deployed into the cohort of occupied homes in the next phase of the project and specifically, the data which would be received from the systems. This included what could be deduced from this data in relation to occupant behaviour. The conventional heating controls installed into one of the test houses mirrored the systems which would be installed in the occupied homes, including individual room temperature sensors and metering sensors. A series of experiments with the heating controls, including changing the heating setpoint, schedules, manual use of the controls and remote access via a mobile phone app were undertaken. This allowed the heating settings to be changed on the digital heating control device in a controlled environment and allowed certain actions to be identified from the data; this included being able to identify the scheduled switching on/off of the heating compared with an occupant interaction with the device in a manual setting. It was also possible to determine when the heating was switched on ahead of the programmed start of the heating schedule, therefore essentially identifying use of the ‘Boost’ function (see Figure 3).

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Figure 3: Daily thermostat set-point trace for test house, showing two scheduled heating times (morning and evening) and a short boost around 11am

Unpicking these characteristics within a controlled environment will allow for identification of these behaviours within the occupied homes, enabling some comparison of reported heating use versus data recorded by the heating controls. It 3

was not possible to distinguish interaction via the mobile phone app from direct interaction with the heating controls, and so an alternative approach for this investigation will be needed.

Phase 2: Occupied homes The first stage of occupied home research was a pilot study conducted with a sample of 12 homes in central England. In each of these homes, the existing heating system controls were replaced with new digital controls capable of both control and feedback. These homes were not undergoing any building retrofit which meant that the impact of the technological intervention could be investigated. Participants were recruited from employees of one of the project’s partner organisations. Whilst this organisation had expertise in energy saving technologies, participants were drawn from the breadth of the organisation, and so included the families of staff from various departments and levels of seniority including secretarial staff, finance clerks, directors etc., many of whom had no energy nor heating technology expertise. A total of 12 households were recruited, all with gas central heating, a broadband connection and were owner-occupiers. Five of the sample had combi-boilers, which provide hot water on demand. The sample households included a range of different property sizes, from one to five bedrooms and from single occupants to a family of six. Approach Once recruited, participants were sent a pack of information along with a questionnaire and a set of calibrated Hobo data loggers, to collect house temperatures prior to the installation of the new controls. Participants were asked to place the data loggers around their home (one per room) according to the instructions provided. These instructions had been designed for this project, to determine whether participants could appropriately place temperature sensors in their homes without expert support. After a period of approximately two weeks, during which time the householders were interviewed and gas/electricity metering equipment added, a new digital heating control device and associated monitoring equipment were installed in each home. This equipment consisted of: a portable zwave digital programmable thermostat featuring a graphical user interface; a wireless boiler relay; a gateway device for enabling the remote access; up to 10 zwave temperature sensors (one per room) for on-going temperature monitoring; a current transformer clamp with z-wave transmitter for electricity metering; and a gas meter pulse counter/z-wave transmitter. These devices were all included on the home wireless network. The heating schedule, heating set-point at five minute intervals and all over-rides could be identified from the recorded data. In addition, the temperature used with the boiler feedback control was recorded at the same time interval. Data management It was intended from the outset that the DEFACTO data would ultimately be made available for secondary analysis, requiring the dataset to not only meet the project needs but also to be completely anonymised, accessible via a wide range of analysis packages and clearly documented. Using a generic data management template from

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the Digital Curation Centre 1 , a project specific plan was devised. This data management plan also records the project progress, including decisions made and constraints which impact on the project. The data are supplied from a total of seven different sources, often via a third party in their chosen standard format and transfer method. All data relating to the household monitoring equipment are sent in CSV format, in hundreds of small files, proving difficult (if not impossible) to use directly. For that reason, the data are transferred into a database and amalgamated using a specialist programming language specifically designed for manipulating data in databases (Structured Query Language). This will enable detailed analysis of the wealth of data during the project, as well as appropriate archiving later on.

Findings As already discussed, this initial, pilot study was designed to be the precursor to a larger study and would test the technical aspects of the study methodology as well as communication systems and processes for use with householders. However, the opportunity was also used to gather information relating to the householders’ use of their heating systems. Whilst this study was not intended to be as robust as larger studies such as that conducted by DECC [8], the results provide preliminary quantitative and qualitative information about the way householders use and understand their heating systems. The monitored data are providing a better understanding of the householder interaction with the heating system, together with the recorded boiler-relay state on boiler firing and attributed gas consumption. Electricity usage in combination with room temperatures can be interpreted to identify the use of different household appliances and the effects of internal gains. Figures 4 and 5 show an example of this from house P08, indicating room thermostat set-point, temperatures in multiple rooms and electricity usage during April 2014.

Figure 4: Heating set-point and zonal temperatures from P08, 5-7 April 2014

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Figure 5: Heating set-point and total electricity consumption, P08, 5-7 April 2014

In Figure 4, the left vertical axis shows the heating set-point chosen by the householder (recorded through the heating control device) and the right vertical axis shows room temperatures from five rooms, recorded over three consecutive days: Saturday 5th April to Monday 7th April. This Figure shows the usual expected temperature increases during heating times (indicated by the change in heating setpoint), as well as an increase in the kitchen and utility room temperatures on the Sunday afternoon. This is followed by a sequential increase in the dining room temperature in the evening, suggesting the cooking, then eating of a meal. Figure 5 further explains these interpretations through the total electricity use (left vertical axis) where increased usage coincides with the temperature increases in the kitchen and utility rooms before the dining room temperature increases. This profile is a typical, expected trend that would be commonly assumed and used within building energy models. In contrast, Figures 4 and 5 show unexpected heating use and internal gains on Monday 8th April which do not correspond to usual heating models. In this case, the occupant stayed at home on the Monday, contradicting the typical weekday-weekend occupancy pattern. This example illustrates the effectiveness of recording the heating set-point, as a proxy for the heating schedule/profile and indicates interpretations which can be made from the different logged quantities. In the preliminary interview, undertaken before their new heating controls were installed, householders were asked which part of their heating system they currently used to control their heating. In the sample, the available controls were a thermostat, boiler controls and TRVs. Only eight of the houses had a thermostat (67%), three of which were digital (25%) and five were analogue (42%). Four houses with an analogue thermostat also had a separate programmer, whilst the remaining house had boiler controls with a timer. Two of the houses (17%) only had boiler controls with which to control their heating. Although five houses had a separate programmer, none used this method of controlling their heating, preferring to use either their TRVs or analogue thermostat instead. For the four households (33%) who did not have a thermostat as part of their heating system controls, the householder had no way of setting the temperature in the household, as demonstrated by the following comments from participants: 6

‘I don’t think [the heating] is very controllable because there’s no room thermostat. When we moved here, it wasn’t even something I thought about. It was only since we moved in and I said, ‘”Where’s the thermostat?” and there just wasn’t one. So there you go, you live and learn.’ [P09, Male, Aged 48] ‘You’re either cold or you’re not. If you’re cold, you turn it up. If you’re not, you turn it down. It seems to work.’ [P08, Male, Aged 44] The latter participant discusses similar principles of controlling his heating to the user type referred to by Rubens & Knowles [8] as a ‘Reactor’. Another of the houses, which displayed characteristics of a ‘Rationer’ [8], used a baby monitor located in a child’s room as their temperature measure for the whole house. Without the benefit of a room thermostat, the householder used the temperature indicator on the baby monitor and only put the heating on when it was too cold for the baby, whilst otherwise trying to keep the use of the heating to a minimum. Participants were also asked about their approach to heating. Two of the households (17%) spoke specifically of heating their home due to the presence of children, corresponding with similar findings of Rubens & Knowles [8], who describe the way people discuss heating use in relation to babies as ‘an extreme instance of people considering others in their heating behaviours’. Another three households in the DEFACTO sample (25%) spoke specifically of heating their home for their pets. This is of particular interest as it highlights that, regardless of the cost or health priorities in relation to their heating, the householders’ concern for pets or small children are likely to override this. One of the householders who controlled their heating around their pets described how they would leave the heating on constantly at 20 degrees if they were to go on holiday during the winter, just for their cats. Five of the households (42%) put their heating on all day; two of these households discussed how they would start with their heating on a timer, but would tend to revert to a constant heating schedule, akin to the user type referred to by Rubens & Knowles [8] as ‘Ego-centric’, for example: ‘Generally, it’s on a timer - the heating - which, to be honest, we tend to override in the winter anyway and just put it on all day, leave it on constant all day.’ [P09, Male, Aged 48] One household discussed having been advised to heat their home constantly, however, they could not remember by whom they had been given this advice: ‘I leave it on constantly at twenty, because I was told it was better to do that rather than turning it on and off all the time.’ [P10, Female, Aged 42] Householders were also asked what they felt was the most efficient way of heating their home: on constant, on demand or via timer. In two of the households (17%), the participants interviewed disagreed on the most efficient way to heat the home. In both of these households, the female participant felt that the most efficient way of heating the home was to keep the heating on constantly, whilst the male participant felt that the most efficient way of heating the home was on demand. In practice, one of the households heated on a demand basis and one had their heating on constantly. In the remaining ten households (67%), three (25%) felt that keeping the heating on constant was the most efficient way of heating their home and two of 7

these households (17%) heated their home in this way. Six households (50%) felt that using the heating on a demand basis was the most efficient way. However, only two of these households (17%) only heated their home in this way, the other households either used their heating on a timer basis or a combination of the timer and on demand. There were four differing approaches to heating in holiday times; there were those that switched their heating off (50%); those that left their heating on as usual (8%); those that reduced the temperature of the heating (25%); and those that reduced the heating times (17%). Six of the households (50%) specified that they would turn the heating off completely if they went away, although three of those (25%) specified that if it was really cold, they would leave it on partially as a frost protection. Two of the households (17%) described how they would leave their heating on whilst they went away, but would reduce the amount of time it was on each day. One household described how they would not change the heating schedule whilst they were on holiday due to the belief that it would ‘mess up’ if it was changed; ‘Just as long as it’s still running, it doesn't mess up.’ [P11, Male, Aged 35]. His partner also described how they would not turn the heating down during these periods because ‘The temperature’s not something we’ve really thought about. We never think to turn that down’ [P11, Female, Aged 34], displaying strong characteristics of a ‘Hands off’ user type [8]. In order to inform future work into zonal control systems, householders were asked whether their ‘spare’ or lesser used rooms were heated differently. Seven of the households (58%) described how the radiators in their lesser used rooms were either turned off or down for the majority of the time, whilst two of the households (17%) heated all rooms the same, regardless of their use, and four of the households (25%) did not have any rooms that were not used regularly. However, seven of the total households (58%) described how they actively used the TRVs on a regular basis to turn down the radiator temperature in rooms not being used specifically at that time, which included some of those who do not have ‘spare’ rooms in their properties. Ahead of having their new heating controls installed, all 12 of the households had at least one participant who felt positively about the use of heating controls with a mobile app, as indicated by the following quotes: ‘I think it's a brilliant idea. I'm one of those very cold people, so say like if I've finished work on a Monday...I normally finish early, so say if I get home at half past four in the winter and it's really cold, obviously my heating hasn't yet come on yet, it doesn't come onto five o'clock. So it would be quite handy in that situation if you had an app so that I could just sort of press the button and it comes on before I sort of come home.’ [P01, Female, Aged 32] ‘I think it’s a brilliant thing, you know, that you can control your heating you know, from wherever you are.’ [P06, Male, Aged 55] ‘Yes, I’m really interested because like I would never know when I’m going to be home, so I can’t set the timer, and I don’t know what time I’m going to be up, so it would really help me in the future. So … if [my flight] was delayed, I’d be like oh my God…my heating’s been on for three hours.’ [P12, Female, Aged 24]

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There were only three participants who discussed uncertainty over this type of technology; for one participant, this was because a lack of confidence in using this type of system whilst for the other two participants, they could not see that they would have any use for the technology, mainly because there was usually someone in the home.

The challenges associated with in-depth analysis and monitoring of heating energy in real homes The main reason for completing a small study in advance of the larger, occupied homes study in this project was to identify the challenges encountered with monitoring and investigating people’s heating habits within their homes and to begin to identify how some of those challenges may be tackled in the main study. Many lessons have been learned and are discussed here. Monitoring the energy used within homes can be disruptive to the occupants which can make it difficult to recruit households for the type of study described here. For this reason, in addition to the failure of the Green Deal to meet expected levels of interest, it proved very difficult to recruit participants for the DEFACTO study. Subsequently, the delays in participant recruitment meant that, by the time equipment was installed in households, it was towards the end of the winter heating season, which had an impact on the data collected. In the next phase of the project, where hundreds of home will be recruited, a professional recruitment company will select and recruit the households, according to a specification, in order to speed up the process and to ensure a monitoring period of at least two winters. In order to assist with the recruitment of participants and to increase participants’ trust in the support provided as part of the project, it was considered important that any information and communication from the project was professional and consistent in its appearance. For this reason, a branding was developed for the study, so that all communications to participants were easily recognisable. In addition, a participant website was developed, along with a dedicated ‘participant hotline’ and email address. Examples of the participant material are shown in Figure 6.

Figure 6: Participant material with consistent branding

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Arranging to visit participants in their homes was challenging and time consuming and often required several phone calls or email correspondences to secure an appropriate time. Due to the working patterns of many of the households, 58% of the interviews had to be carried out in the evening, outside office hours, therefore requiring the research team to be flexible. In addition, where possible, it was intended that both adult members of the household would be interviewed in order to capture the differing opinions and attitudes around heating use within a household, however in four of the ten houses where there was more than one adult in the household, it was only possible to interview one of the adult household members due to availability. Once the interview had taken place, the need for organisation and scheduling continued as the installers had to arrange an appropriate time with the householder to install the new system. Some of the early installations took a considerable amount of time and required a second visit by the installer, which not only required more organisation but also caused more disruption and inconvenience to the householders. There was a significant learning process in the installation of the monitoring systems which had not been accounted for; initial installs took up to five hours, however by the final installations, this was reduced significantly to less than two hours. In the main study, this learning process is accounted for in the recruitment process, where households will be selected in one geographical cluster to enable a smaller number of installers to carry out the installs and therefore reduce the learning time required. Reliable monitoring proved significantly more difficult in occupied houses than in the test houses, not only due to the disturbance caused to householders, but also due to the way householders and their behaviour impacted on the monitoring equipment. For example, householders moved the temperature sensors when cleaning or decorating, causing anomalies in the results and whilst the original position of the sensors had been checked, on a later visit some had been moved, with some being placed in inappropriate locations, such as on window sills (i.e. near direct sunlight and draughts). In addition, whilst it seemed possible to place sensors out of the way of small children, cats provided more of an issue! In the main study, stickers will be designed for the temperature sensors with reminders of their ideal placement and householders will be given more detailed information about them and the other monitoring equipment. Gas metering was carried out in the houses with a pulse output using a z-wave gas pulse counter and transmitter. In the planning and testing phase, this device was tested both in direct connection to the gas meter pulse socket and with an optical meter reader in between. The latter aimed at finding a solution for houses with gas meters without pulse output (eight of the 12 house sample), however, this did not provide satisfactory results when a comparison with the meter reading was made. Furthermore, the device tested was no longer produced by the manufacturer and no future replacement was expected. Therefore, the only feasible solution was to use a third-party service to replace the household’s gas meter, which would then provide half hourly data. This has incurred significant cost to the project and means that there is further disruption to householders as another visit is required to facilitate the changing of the gas meter; however, it does provide detailed gas consumption data. The most significant challenge in the monitoring of households related to the robustness of the wireless sensor network. The wireless sensors operate on a z10

wave frequency, which has only been established in the last decade and is therefore still under development. One of the main issues encountered is missing data, both on a permanent and temporary basis, which is most often caused by radio frequency noise, range and the dynamic nature of the house environment (including human behavioural interferences). During the study, eight of the 12 houses (67%) lost data from at least one sensor. In addition to these challenges, as the pilot study progressed, it became clear that the electricity meter, which was being deployed as part of the monitoring kit, had very poor battery life and so a decision was made to change the type of electricity meter. However, before a replacement could be made, spare batteries had to be posted out to two households, along with instructions of how batteries should be replaced in the meter; even so, only one of the households actually replaced the batteries supplied to them. In addition to the difficulties experienced with the monitoring equipment within the homes, some of the households also had queries relating to the use of the mobile app to control their heating. As a result of all of these different issues, there was additional contact required post-installation with eight (67%) of the households, resulting in additional hassle to the householders and an increase in workload for those in the research team. A significant part of the investigation into occupied homes was aimed at understanding the householders’ use and understanding of their heating system. However, in some cases, this proved challenging as householders struggled to discuss unconscious habits relating to their heating use. In addition, several of the participants became confused when trying to discuss their heating system, particularly in relation to the different names of the parts. However, this confusion provides useful feedback to system developers who need to use appropriate language when detailing parts of the heating system to householders. The research team had very little control over the way data were received, with the majority having to be manually downloaded from secure third party websites, whilst some were sent via email. The manual download of data involved a considerable time commitment from the research team, requiring a minimum of 30 minutes per house, every other week. This time commitment was initially managed by dividing the task between two team members. The manual download was necessary for learning about the transfer process and feasible during the testing stages and pilot study, but this time commitment was deemed to be excessive for the main study, once hundreds of homes are recruited. For this reason, the development of a computer program has been commissioned to facilitate and perform the automatic download of sensor data through a single website. However, due to the wide variety of data sources it is not possible to automate all of the data downloads, illustrating one of the hidden time and financial costs of domestic energy studies involving real homes. Whilst there were many challenges to be overcome ahead of the main study, as discussed here, the project is developing a successful way of monitoring the heating energy use of households.

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Conclusion The DEFACTO study has already started to provide interesting insights into the heating use of a small number of households, supported by evidence from the test house trials. The main learning from the pilot study, however, has related to the complexity of conducting a detailed quantitative and qualitative study of the heating energy use of hundreds of UK homes. Methods trialled in the pilot study have highlighted key challenges, not least in the attempt to understand the heating use of a householder from an interdisciplinary perspective, and these methods are being refined to be effective at scale. The main study promises to offer an insight into people’s use of heating with digital feedback and control at a scale rarely undertaken and the challenges highlighted here indicate why this might be.

References [1] Palmer J, & Cooper I (2013) United Kingdom housing energy fact file, 2013. Department of Energy and Climate Change, London, UK. [2] Lomas, K. J. (2010) Carbon reduction in existing buildings: A transdisciplinary approach. Building Research & Information, 38(1), pp. 1-11. Available at: http://dx.doi.org/10.1080/09613210903350937. [3] Stafford, A. & Lilley, D. (2012) Predicting in situ heat pump performance: An investigation into a single ground-source heat pump system in the context of 10 similar systems. Energy and Buildings, 49, pp. 536-541. [4] ATTMA. Technical Standard L1: Measuring air permeability of building envelopes (dwellings). The Air Tightness Testing & Measurement Association; 2010. Available online at: www.attma.org/wp-content/uploads/2013/10/ATTMA-TSL1-Issue-1.pdf. [5] Wingfield J, Johnston D, Miles-Shenton D & Bell M. (2010) Whole house heat loss test method (Co heating). Leeds Metropolitan University. Available online at: www.leedsmet.ac.uk/as/cebe/projects/iea_annex58/whole_house_heat_loss_test_m ethom(coheating).pdf. [6] Department for Communities and Local Government. (2011) Domestic Building Services Compliance Guide (2010 Edition). HM Government. Available online at: www.planningportal.gov.uk/uploads/br/domestic_building_compliance_guide_2010.p df. [7] Office for National Statistics (2003). United Kingdom Time Use Survey, 2000. Online: Available to registered users from: www.esds.ac.uk/findingData/snDescription.asp?sn=4504. [8] Rubens, S., Knowles, J. (2013). What people want from their heating controls: a qualitative study. A report to the Department for Energy and Climate Change. new experience. DECC, London.

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