A simple, scalable and low-cost method to generate thermal diagnostics of a domestic building

Applied Energy 134 (2014) 519–530

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Applied Energy journal homepage: www.elsevier.com/locate/apenergy

A simple, scalable and low-cost method to generate thermal diagnostics of a domestic building Anastasios Papafragkou a,⇑, Siddhartha Ghosh b, Patrick A.B. James a, Alex Rogers b, AbuBakr S. Bahaj a a b

Faculty of Engineering and the Environment, Lanchester Building, University of Southampton, SO17 1BJ, UK Electronics and Computer Science, Building 32, University of Southampton, SO17 1BJ, UK

h i g h l i g h t s  Our diagnostic method uses a single field measurement from a temperature logger.  Building technical performance and occupant behaviour are addressed simultaneously.  Our algorithm learns a thermal model of a home and diagnoses the heating system.  We propose a novel clustering approach to decouple user behaviour from technical performance.  Our diagnostic confidence is enhanced using a large scale deployment.

a r t i c l e

i n f o

Article history: Received 26 September 2013 Received in revised form 5 August 2014 Accepted 7 August 2014 Available online 6 September 2014 Keywords: Domestic heating Thermal modelling Heating and ventilation Occupant preferences Energy feedback Building energy performance

a b s t r a c t Traditional approaches to understand the problem of the energy performance in the domestic sector include on-site surveys by energy assessors and the installation of complex home energy monitoring systems. The time and money that needs to be invested by the occupants and the form of feedback generated by these approaches often makes them unattractive to householders. This paper demonstrates a simple, low cost method that generates thermal diagnostics for dwellings, measuring only one field dataset; internal temperature over a period of 1 week. A thermal model, which is essentially a learning algorithm, generates a set of thermal diagnostics about the primary heating system, the occupants’ preferences and the impact of certain interventions, such as lowering the thermostat set-point. A simple clustering approach is also proposed to categorise homes according to their building fabric thermal performance and occupants’ energy efficiency with respect to ventilation. The advantage of this clustering approach is that the occupants receive tailored advice on certain actions that if taken will improve the overall thermal performance of a dwelling. Due to the method’s low cost and simplicity it could facilitate government initiatives, such as the ‘Green Deal’ in the UK. Ó 2014 Elsevier Ltd. All rights reserved.

1. Introduction In 2011, the domestic sector was one of the most significant consumers of energy, which accounted for approximately 28% of total energy consumption in the UK. In the same year, space heating accounted for 60% of total domestic consumption, underlying the potential energy savings that can be achieved from addressing space heating alone [1]. The UK’s building stock is characterised by poor energy performance. According to the Department for Communities and Local Government [2] the average Energy Efficiency Rating (EER) for 2010 was 55 on a scale of 1–100. The slow replacement rate of ⇑ Corresponding author. Tel.: +44 2380 593940. E-mail address: [email protected] (A. Papafragkou). http://dx.doi.org/10.1016/j.apenergy.2014.08.045 0306-2619/Ó 2014 Elsevier Ltd. All rights reserved.

the existing building stock, which is estimated to be less than 1% per year [3], highlights the importance of the existing building stock in meeting climate change objectives. The UK Government started to address the energy efficiency problem of the UK’s housing stock by introducing the Standard Assessment Procedure (SAP) in 1995 [4], by continuously tightening Part L (Conservation of Fuel and Power) of the building regulations [5] and more recently by introducing the ‘Green Deal’ [6], an underwritten loan scheme to subsidise energy efficiency measures in homes. Due to continuous efforts, domestic energy consumption in the UK has decreased by 5% between 1990 and 2011; principally as a result of heating efficiency improvements and insulation measures [7]. However, according to the English housing survey stock in 2010 [8], 27% of all existing dwellings built post-1900 still had no cavity wall insulation and 42% had loft insulation less than

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150 mm, when the recommended depth for mineral wool insulation in the UK is 270 mm [9]. It is clear that there is room to further improve the energy performance of the UK building stock from even the simplest of measures. It has though been observed through numerous studies that energy savings achieved through energy efficiency measures can be displaced by a set of behaviours known as the rebound effect [10–14]. This results in lower than expected actual energy savings and potentially higher carbon emissions than the measures were expected to save. Studies have shown that the rebound effect for household space heating lies in the region of 20–30%, with some cases exceeding 50% [10,12]. For cold homes in particular, the benefits of energy efficiency correspond to improved thermal comfort and not necessarily to actual energy reduction [13,14]. It is generally accepted that energy is an ‘invisible’ commodity to the majority of homeowners. Most occupants receive very little information on their energy use, beyond a 3 month period bill, which in many cases is paid through a direct debit making energy far less ‘visible’. It is envisaged that the ‘visibility’ problem will be addressed to a certain extent by real time information through smart-meters. In the light of this, the UK Government has announced an ambitious smart meter programme to be launched in 2015, where, by the end of 2020, every home in Great Britain will have a smart electricity and gas meter with an in-home display [15]. The ambition of Department of Energy and Climate Change (DECC) is that the real time information provided by these meters will result in lower energy consumption, lower associated emissions and lower energy bills. In the longer term, consumption may be energy neutral but carbon emissions should reduce due to load shifting through time of use pricing. Consumers will benefit by having direct access to information that will help them understand and manage their energy use [15]. At the same time they will be able to share their data with third parties, enabling the energy industry to operate more efficiently by supporting the development of competition in Britain’s energy markets [16]. The consensus of the above literature is that achieving sustained energy consumption reduction implies a combination of technological interventions and behavioural changes. When targeting individual households however, the following question arises; should we first focus on improving the building’s technical energy performance, occupants’ behaviour or do these two elements only work in combination? Several models focusing on buildings’ technical energy performance or on the behaviour of their occupants with respect to energy consumption have been developed and suggested in literature, whilst some of these models have been used as the basis for the development of tools supporting Governmental energy policies [17–19]. Occupants’ activity is often treated as noise in a model [20] or is taken into account by adopting a set of assumptions [21]. Traditional approaches to address the poor technical energy performance of buildings often require extensive measurements and detailed datasets obtained through on-site surveys performed by accredited energy assessors. In result, such approaches have an increased initial financial cost making related Government initiatives, such as the ‘Green Deal’ in the UK, potentially unattractive to householders. In addition, there has been criticism on the usefulness and reliability of the results generated by such assessments [14,22–24]. To address problems related to occupants’ behaviour various commercial household monitoring and energy management systems have been developed. Such systems usually incorporate a number of sensors, which collect data over a long period. Through an interface or a digital display it is possible to provide direct feedback to the users in the form of actual energy consumption or cost. Such systems are often characterised by significant capital cost and complex controls that users find difficult to understand and

therefore do not engage with them. Literature has also shown that the observed savings achieved through the installation of energy monitoring systems can vary significantly according to the form of the feedback provided to the occupants, with direct feedback being more effective than indirect feedback [25]. Beyond monitoring, the potential of other advanced control systems in mitigating energy use in homes has been investigated [26]. However, Shipworth et al. [27], using field data, have demonstrated that households using central heating system controls do not result in lower internal temperatures or durations of operation, and therefore reduced mean energy consumption, compared with households that do not use such controls. It becomes apparent that a holistic, but at the same time simple, approach that addresses both the poor technical performance of a building and inefficient energy use behaviours and provides the occupants and the policymakers with specific feedback and advice is necessary.

1.1. Aim and scope of paper The purpose of this paper is to demonstrate a simple, reliable and low cost method to understand the heating issues of a property and generate a set of thermal diagnostics focusing on specific aspects of occupants’ behaviour and a building’s technical thermal performance. Recommendations may be as simple as changing the heating and ventilation strategy or more radical interventions such as upgrading the building’s level of insulation. The advantage of the methodology presented in this paper over other approaches is that it addresses heating issues related to a building’s thermal performance and its occupants’ behaviour at the same time, based on a single field dataset; internal temperature. It is a low cost, non-intrusive method characterised by low complexity. It does not incorporate complex monitoring systems which require occupants to invest time to understand and benefit from them or detailed on-site building surveys conducted by engineers and assessors. Most importantly, it can identify the root cause of space heating energy wastage if related to the building’s technical performance and/or the occupants’ behaviour. In the work described below we develop a thermal model based on a single field dataset which generates a set of thermal diagnostics for domestic buildings. These diagnostics include information relating to (i) the building’s heat loss through the building fabric, (ii) the building’s heat loss due to ventilation and (iii) the occupant’s preferences with respect to central heating. Using multiple temperature decay rates (the rate at which the internal temperature decays overnight) and the overall standard deviation for the same building, we propose an approach to cluster buildings in four categories; (1) ‘Thermally leaky, low variability’, (2) ‘Thermally leaky, high variability’, (3) ‘Thermally tight, low variability’ and (4) ‘Thermally tight, high variability’. These categories provide information on a dwelling’s overall thermal performance and whether the root cause of heat loss is principally building fabric related or behaviour related. We suggest that this ‘clustering approach’ could displace on-site, costly and time consuming domestic energy surveys.

2. Methodology Our method relies on internal temperature data recorded over a period of 1 week, subject to heating system being on. Previous work from Bacher and Madsen [28] has demonstrated that 1 week of monitoring is sufficient to provide good results. Based on the weekly temperature profiles collected, a thermal model was developed and a list of heating diagnostics was generated for each of the

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25 homes in this study. These can be split in the following two categories: (1) Primary heating system settings (on/off times, runtime duration, thermostat set-point). (2) Temperature decay rate. To enable validation, the analysis was performed for homes where further data such as building type, level of insulation, property age, building fabric construction characteristics, heating system specifications and occupancy profile were available. For the purposes of this paper, only one field dataset was used for the analysis; the temperature collected from a sensor placed next to the thermostat that controls the central heating. We demonstrate that a single field dataset obtained using a data logger is sufficient to generate useful feedback that would otherwise require more complex monitoring equipment and/or on-site surveys. Table 1 summarises the datasets used for our method and the datasets used for validation. The use of a single temperature measurement (single node) is not fully representative of the load of the heating system; however it still allows the identification of a set of parameters that describe the heating profile of a household. A characteristic example is central heating systems controlled in part by Thermostatic Radiator Valves (TRVs) which is a common configuration in the UK. TRVs may have an impact on the average rate of combustion of gas; however the boiler’s on/off events will still be controlled by the thermostat. With or without the presence of TRVs, it is possible to identify a set of parameters such as temperature decay rate, heating periods, and overheating. On the other hand, the presence of TRVs would pose serious limitations in quantifying potential energy savings using this methodology. 2.1. Data collection The monitored houses form part of 200 households monitored for the ‘‘The role of community-based initiatives in energy saving’’, a 4 year ESRC study that seeks to assess the impact of community greening groups on a roll out program insulation upgrades in privately owned housing [29,30]. These houses are located in a suburban area located to the North of Southampton in Hampshire in the South of the UK. This area is classified by the Office for National Statistics (ONS) as 4a2: ‘Prospering young families’ [31]. Areas with this classification are close to the national average for the proportion of people working at home, the number of people per room and population density. According to the ONS similar areas are spread evenly across the UK [31]. A specially designed data logger with an integrated temperature sensor was posted to each one of the 25 houses, along with instructions of where to place it and how to activate it. The temperature logger was developed around an Atmel AT90USB162 microcontroller with a Texas Instruments TMP275 temperature sensor. This provides ±0.2 °C measurement accuracy without requiring additional calibration. The logger is triggered by a single button

and records temperature at a 2 min interval. It automatically stops after it has recorded data for 7 days, a total of 5040 measurements. Fig. 1 shows the temperature logger which has the size of a USB flash drive. Householders were asked to place the temperature logger next to the thermostat that controls the central heating. A comprehensive supporting dataset had already been collected through monitoring and on-site surveys and was used to validate the models developed. Temperature data were collected using a household monitoring system provided by AlertMe [32]. The AlertMe system provides ±0.25 °C accuracy and records temperature data at a 2 min interval. It consists of three temperature sensors, a power meter, an in-home display and a data hub. The data collection with the monitoring equipment proved to be a time consuming process with many practical challenges. Dealing with householders, regular equipment failures in the field, loss of data due to poor broadband quality, interference with other WiFi devices in the house and high on-going maintenance were some of the most important challenges faced during the field trial. Data that enabled a full SAP rating assessment were collected through on-site surveys. Of the 200 houses in the CBES study a sub-set of 25 houses was chosen with good data availability and geographical proximity. Ambient temperature data were obtained from the Weather Underground website at a 2 min interval [33]. 2.2. Lounge temperature versus thermostat temperature Thermostats are often placed in transit spaces in houses. A key question therefore is whether the temperature profile at such a location is representative of living space such as the lounge. Fig. 2 shows a 6 day temperature profile for one house as recorded by the AlertMe sensor placed in the lounge and by the temperature logger placed next to the central heating thermostat. Data for only 6 days have been plotted, starting from midnight of the first day. It is clear that the lounge temperature is higher than the temperature in the room where the thermostat is located. For the example presented here the maximum temperature difference observed was 1.5 °C. Similar temperature difference was observed for most of the houses participated in this study. This temperature difference is characteristic of thermostats being located in cold transit spaces of the house (e.g. hallway). Lounges in contrast may have a higher average temperature due to

Table 1 Method and validation datasets used in this paper. Method dataset (i) Data-logger: Temperature at thermostat (2 min) (ii) Weather data (2 min)

Validation dataset (i) AlertMe system: Temperature at lounge (2 min) Temperature at boiler (2 min) (ii) Weather data (2 min) (iii) Building survey as per SAP

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Fig. 1. Temperature logger placed next to a central heating thermostat.

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Fig. 2. Thermostat and lounge temperature profile over a 6 day period, from Friday 09/03/2012 at 00:00 to Wednesday 14/03/2012 at 23:59.

secondary heating, increased internal gains and lower ventilation. Higher lounge temperature could also be observed due to differences in radiator sizing and balancing through lock-shield valves. If that is the case then this temperature difference is a diagnostic finding in itself. Both datasets do however reveal the same heating profile. Very similar heating cycles can be identified and decay curves with the same profile can be observed during the night hours on both datasets. The difference between the thermostat set-point and the temperature measured at the thermostat is the variable which controls the central heating system. Consequently, temperatures recorded next to the thermostat are more representative of the boiler’s state than temperatures recorded in any other room in the house. 3. Thermal diagnostics In the following two sections the algorithm developed to generate the heating system diagnostics and the building’s temperature decay rate is described. Our algorithm first learns the thermal properties of the home and the operational settings of the heating system. Based on the learned parameters it then calculates the effect of interventions, such as selecting a lower thermostat setpoint, on the overall energy consumption for space heating. The algorithm’s output enables a clustering approach to the analysis, to cluster homes in four categories. These clusters indicate houses with comparable heat loss characteristics (well or poorly insulated) and similar efficiency of the users’ behaviour with respect to ventilation. 3.1. Diagnosing the primary heating system In the UK around 90% of the dwellings have central heating systems installed [2]. For 85% of the dwellings the primary heating system is a gas fired boiler, usually controlled by a programmer and a single thermostat. It has been observed that in many cases occupants misuse these controllers either because they have a different perception of how the heating system responds to these controllers or because they find them too complicated. Critchley et al. [34], in their study of cold homes, report that a third of their respondents over 60 years old faced difficulty with programmers, with a majority of these saying they were too complicated. Boait and Rylatt [35] suggest that complex controllers with inconvenient push buttons or complicated menus can often be challenging to users, whilst Shipworth et al. suggest that such controls may have no impact on mean energy consumption. Users often choose heating profiles that do not match their heating preferences and occupancy or in other cases switch to manual operation. Such behaviours may result in the heating being left on when it is not needed or in overheating and as a consequence, significant heat wastage. A typical example of this, are occupants turning the heating off very late in the night. This term of ‘over-running’ of the heating system fails to take into account the building’s thermal capacitance

and high temperatures in the living areas, when these are not occupied, may occur. A similar phenomenon is observed when occupants select a high thermostat set point; again, failing to understand the building’s thermal response, the internal temperature increases, occasionally causing discomfort. Occupants are more likely to take action only when they perceive some discomfort. In terms of energy efficiency operations it is ‘too late’ at this point. Lowering the thermostat set-point is often followed by opening windows; an energy-wasteful action but with an immediate effect. We have developed an algorithm that learns the heating profile of each dwelling based on the recorded internal temperatures. The term ‘heating profile’ includes the heating cycles (switch-on and switch-off times) and the thermostat set-point selected by the occupants. Based on the heating profile learned by our algorithm, unusually high set-point settings can be flagged and overheating warnings can be generated. Prolonged heating cycles that result in high temperatures during the night when occupants are most likely to be sleeping can also be flagged. As a second stage, the same heating profile and a lower thermostat set-point are then used as inputs by our algorithm to quantify the theoretical impact of a lower thermostat set-point on the overall heating consumption. Specifically, we calculate the impact of reducing the set-point either by one degree, or to value of 19 °C. Although the algorithm does not account for boiler modulation, it can still capture the heating practises followed by the occupants. Whether the boiler modulates or not, the start time of a heating cycle will be followed by a prolonged pre-heat curve; accordingly the end time will be followed by a prolonged temperature decay curve. Domestic hot water demand however, may have an impact on the identification of the heating cycles. For the case of combination boilers, without a hot water cylinder, hot water demand has been eliminated from the analysis by setting the algorithm to exclude preheat curves shorter than 15 min. It is assumed that such short intervals will only correspond to hot water demand and not to demand for space heating. For the case of a regular boiler the hot water demand is inevitably included in the analysis but only for those cases where hot water and the central heating are programmed to come on separately. From the 25 homes used in this paper, only 3 said to program the hot water on different times to space heating, whilst another 10 homes had a combination boiler without hot water cylinder. Table 2 summarises the list of diagnostics that are generated by our algorithm. We use a standard building thermal model, where heat leaks from the home at a rate proportional to the difference between internal and external temperatures [33]. This model can be expressed as a discrete stochastic difference equation given by Eq. (1):

T i ðt þ 1Þ ¼ T i ðtÞ þ ½r p  r h ðtÞ  aðT i ðtÞ  T ext ðtÞÞDt þ eðtÞ

ð1Þ

where at time t, Ti are the estimated internal temperature (°C), Text are the interpolated external temperatures (°C), rh is a binary

A. Papafragkou et al. / Applied Energy 134 (2014) 519–530 Table 2 Primary heating system diagnostics and feedback messages generated by the model. Diagnostic

Feedback

Heating cycles

 Programmer settings  Warning for late boiler-off event  Potential savings during night time

Thermostat set-point

   

Thermostat set-point identification Overheating warnings Impact of a lower thermostat set-point Boiler failing to achieve the set-point

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convex optimisation problem using the interior-point algorithm with conjugate gradient steps [36]. The external temperature measurements used for the thermal modelling is from the closest live weather station to a home (from www.weatherunderground.com). However, since these temperature measurements are not time aligned with the internal temperature measurements and often exhibit periods of missing data, we use regression to interpolate the temperature readings and complete any missing periods, which yields Text. 3.2. Temperature decay rate

variable which indicates whether the heating system is on or off, rp is the heater output (°C/h), a is the coefficient of temperature decay (1/h), Dt is the time interval (1/30 h in this case) and e is Gaussian noise capturing un-modelled thermal effects. Since the data logger is placed on top of the home’s thermostat, the recorded temperature is strongly dependent on the operational characteristics of the home’s heating system. We model the heating system by assuming that the thermostat has a single set-point temperature throughout the day, and that a separate timer, which allows either one or two heating periods per day, controls the boiler. This is a common configuration in UK homes with a single zone heating system. We perform inference of the thermal properties of the home, and the operational settings of the heating system, by defining the parameter vector h given by h = [rp, a, Tset, s1, e1, s2, e2, m], where rp and a are the heater output and the temperature decay coefficient from the thermal model, si and ei are the start and end times of each heating cycle, and m e {1, 2} is the operational mode of the heating system (either one or two heating cycles per day). We then use these parameters to estimate the internal temperature over the logging period, by initialising our first estimate of the internal temperature to be equal to the first temperature logger measurement (Ti(1) = Tlog(1)). Using Algorithm 1 (presented in Fig. 3) we iteratively propagate the thermal model forward, updating the internal temperature estimate given the thermal performance of the home and the real-time control policy of the heating system (using the thermostat setting, the internal temperature and the timer setting to determine rh(t) in each time period). The optimal parameter vector, h⁄ is that which minimises the squared error between the estimated internal temperature measurements and those actually recorded by the logger (Tlog), and is given by Eq. (2):

h ¼ argminh

N X ðT iðtÞ  T logðtÞ Þ2

ð2Þ

t¼1

where argmin stands for the set of points of the given argument for which the given function attains its minimum value. This objective function is convex and quadratic with added constraints that, rh(t) e {0, 1} and m e {1, 2}, and thus, we solve this constrained

The overall thermal performance of the building envelope can be assessed based on the total temperature decay rate during periods when there is no heating output. During those periods the internal temperature drops approximately exponentially and a ‘decay curve’ can be identified by its prolonged negative gradient. In Fig. 4 two of these decay curves for the same house have been plotted over a 2-day period. The rate a (1/h) of the exponential decay can be partly represented by the time constant of the building zone, s, expressed in hours [37]:

s ¼ 1=a ¼ ðC m =3600Þ=U 0 ¼ ðC m =3600Þ=ðHtr;adj þ Hv e;adj Þ

ð3Þ

where U0 is the overall heat loss (W/K) and (Cm) is the building’s effective thermal capacitance (J/K), Htr,adj is the overall heat transfer coefficient by transmission and Hve,adj the overall heat transfer coefficient by ventilation. The time constant of the building zone, s, depends on the thermal transmittance of the envelope, the ventilation, the effective thermal capacitance and the internal gains. The effect of the thermal transmittance can be assessed by decomposing the effect of each factor on the decay curve. The decomposition approach was undertaken by focusing on the overnight periods and by making the following assumptions:  For a decay curve that occurs during the night, the internal gains near the thermostat are assumed to be very small and therefore can be neglected.  Occupant activity is minimised during the night. Occupants are less likely to open/close windows or doors during the night, therefore occupant induced ventilation is assumed to be constant during the night.  Infiltration rate is assumed to be constant overnight for the same building. This is only valid when there is no change in the direction and the speed of the prevailing winds. The wind effect can be assumed to be negligible when wind speeds are low, especially for urban/sub-urban well sheltered locations such as this case study area. To further support this assumption a correlation analysis was performed between the estimated heat leakage rates and the wind speeds during the monitoring week.  There is no significant variation into the building’s state prior to the decay. This can be justified by a consistent periodic heating profile in terms of both temperature set-point and schedule. To ensure this, the algorithm is set to run only if the learned heating profile parameters of the last 48 h prior to the decay are similar. Taking into account the above assumptions, a night decay curve and similarly the temperature decay rate a can be expressed as a function of:

a ¼ f ðthermal transmittanceÞ þ V; where V ¼ Constant for ventilation Fig. 3. Algorithm iteratively propagate the thermal model forward.

ð4Þ

A constant ventilation rate assumes fixed conditions during the observed period; however, it does not eliminate the impact of

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Fig. 4. Night decay curves over a 2 day period. Shaded blocks represent the night decay periods.

actions that occupants may have taken before that. For example if occupants have opened a window and this was left open during the entire night, the estimated temperature decay rate a will be significantly higher than it would be if the same window were shut. To de-couple the impact of ventilation and thermal transmittance on the decay curve, the night-time temperature decay rates for each day over the monitoring period for each house were estimated and the standard deviation across the sample was calculated. Short decay rates denote leaky buildings, but do not give any information on whether this is the result of poor building fabric or the result of high ventilation rates. Long decay rates denote a well-insulated house and low ventilation rates at the same time. The standard deviation of the night temperature decay rates of each sample is strongly related to the variations introduced by ventilation. Other factors such as thermal transmittance, infiltration and overnight internal gains are essentially independent of the householder and therefore are assumed constant. A low standard deviation value suggests a consistent ventilation profile, whilst the opposite suggests that human behaviour has had an impact on the overall ventilation rate. By combining these two pieces of information it is possible to generate a set of energy diagnostics for each house. As shown in Fig. 5 there are four possible outcomes and each house can be classified into one of these four categories. Category 1: ‘Thermally leaky, low variability’. Houses in this category are very likely to have a poor building fabric. The thermal performance of the building can be improved by upgrading insulation measures, such as loft or cavity wall insulation. High temperature decay rates could also be the result of high ventilation rates, such as windows being left open constantly. If this is not the case, then efforts should initially focus on improving the building fabric. There is high potential for improvement. Category 2: ‘Thermally leaky, high variability’. In this category houses have variable ventilation rates, resulting in heat losses that can be avoided through a better ventilation strategy. Therefore, occupants should first focus on changing their ventilation habits. If temperature decay rates still remain high, occupants should consider upgrading the building envelope. There is high potential for improvement. Category 3: ‘Thermally tight, high variability’. Although houses in this category do not appear to be leaky, the ventilation is not optimum. The overall thermal performance can be further improved if occupants change their ventilation habits. There is some potential for improvement. Category 4: ‘Thermally tight, low variability’. This category appears to have the highest thermal performance. Houses of this category are the least likely to need any building fabric upgrades and occupants appear to have adopted a good ventilation strategy that does not result in heat wastage. There is very limited potential for improvement. To validate our approach, the average temperature decay rate of each category was compared with the overall heat loss coefficient through the building fabric. Comprehensive data about the overall

infiltration (draught proofing, draught lobby, window seals, passive vents, chimneys, flues) and the building fabric were already available through the in-house inspection SAP1 surveys undertaken for the ‘Energy and Communities’ study. This comprehensive dataset allowed the estimation of the overall fabric heat loss coefficient for all the buildings participated in this study. For those building characteristics where on-site surveys did not allow the direct estimation of the heat loss coefficient (for instance thermal bridging), the default SAP values were used. It should be noted that the sum of an envelope’s elemental u-values is not always the same as the ‘as built’ sum of u-values. This is usually due to poor construction details and will result in an energy performance gap. It is anticipated however, that even if such an effect is present, the ranked order of buildings based on the overall temperature decay rate would remain broadly unchanged. 4. Experimental evaluation The performance of our algorithm and the proposed clustering approach are demonstrated in the following sections. In detail, we evaluate the algorithm’s performance (a) in learning the heating system’s settings as selected by a user, (b) in calculating the building’s temperature decay rate and (c) in calculating the impact of interventions, such as lowering the thermostat set-point when this is higher than the recommended one. One example for each generated diagnostic is presented, followed by a validation method using comprehensive datasets that were already available. The entire sample of 25 homes is then clustered in four categories using our clustering approach and houses with the potential to improve their overall thermal performance are identified. 4.1. Primary heating system diagnostics Fig. 6 summarises the heating cycles as identified by the algorithm for one of the monitored houses. The algorithm has identified the boiler on/off events based on the internal temperature recorded next to the thermostat and the total heating time for the monitored week was found to be 56 h. The boiler temperature profile, which was available through the household monitoring system (AlertMe), was used to validate the model. The heating cycles were manually identified using the boiler temperature data and the total heating time for the same example was found to be 55 h. The agreement between the algorithm and the manual identification show that the heating cycles and in particular the boileron events have been identified with very good accuracy. For the entire set of houses, the average discrepancy between the results obtained through the algorithm (temperature at thermostat location) and the results obtained manually (boiler housing 1 Standard Assessment Procedure (SAP) is the UK Government’s methodology for assessing and comparing the energy performance of dwellings. In addition, it is used for building regulation compliance for new dwellings in England and Wales [4].

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Fig. 5. Schematic of the approach followed to de-couple the effect of the building’s thermal transmittance and ventilation on the temperature decay curve. ‘a.’ and ‘b.’ indicate the priority order of the most likely root cause of heat loss.

Fig. 6. Heating cycles based on indoor temperatures as identified by the algorithm (a), heating cycles based on boiler temperatures identified manually (b) and ambient temperature profile (c) from Tuesday 06/03/2012 at 16:46 to Tuesday 13/03/2012 at 16:44. The red vertical lines indicate the boiler-on time and the green vertical lines indicate the boiler-off time events. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

temperature) was 5.4 ± 2.4% in terms of total heating time during 1 week. The maximum discrepancy observed was 12% and the minimum was 0.7%, indicating a very good agreement. Such discrepancies can be attributed to various factors that would not impact on both temperature datasets (thermostat compared to

boiler), such as secondary heating, imbalanced ventilation rates and internal gains. Fig. 7 illustrates an example where late ‘boiler-off’ events have been identified and flagged (shown in red). For 3 nights out of 4 that have been flagged, the indoor temperature does not drop

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Fig. 7. Indoor temperature profile where late switch-off boiler times have been flagged (highlighted in red), from Tuesday 06/03/2012 at 16:46 to Tuesday 13/03/2012 at 16:44. (For interpretation of the references to colour in this figure legend, the reader is referred to the web version of this article.)

below 19 °C throughout the night. For this example, the total heating time for the monitored week has been estimated at 54 h. Reducing the total time that the central heating is on by 5 h, would result in a 9.2% savings without disturbing the thermal comfort of the occupants. Assuming the same heating profile repeats every week, the potential annual savings from switching off the central heating earlier could be significant. For this calculation it was assumed that the extension of the heating does not provide any thermal benefit to the following morning heating cycle. This assumption is likely to be valid for thermally leaky houses. Fig. 8a shows the algorithm’s performance in identifying the effective thermostat set-point. For the example presented the set-point was found to be 20 °C. The areas between the ‘Actual’ line (blue) and the ‘Estimate’ line (black) indicate the divergence of the system’s performance from an optimum theoretical performance. The term ‘system’ includes the heating system, the building and the occupants. During the heating cycles this divergence represents potential savings or under-performance.

When the ‘Actual’ line exceeds the ‘Estimate’ line, the area between the lines represents potential savings. Here the thermostat’s deadband hysteresis is a major component of this variability. A relatively insensitive thermostat with a symmetrical deadband would result in temperatures higher than the thermostat setting more regularly than a thermostat with a low deadband hysteresis. In Fig. 8a the area highlighted in red clearly exceeds the thermostat’s deadband hysteresis, whilst the indoor temperature exceeds the thermostat set-point setting by 1 °C. This is an overheating event, most likely triggered by the high ambient temperature during that period (18 °C). When the ‘Actual’ line is lower than the ‘Estimate’ line, the heating system under-performs and fails to attain the thermostat setpoint chosen by a user. This could be attributed to high ventilation rates, short heating cycles set by a household, very low ambient temperature or undersized boiler. Next, the impact of a lower set-point on the energy consumption for space heating and the thermal comfort of the occupants

Fig. 8. Identification of the effective thermostat set-point temperature, overheating or under-performance (a), impact of a reduced thermostat set-point (b) and ambient temperature profile (c) from Friday 16/03/2012 at 00:00 to Wednesday 14/03/2012 at 23:59.

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set-point and lower heat losses through the building fabric due to a lower Dh with ambient. 4.2. Building fabric, ventilation and heat decay diagnostics

Fig. 9. Average hourly wind speeds (m/s) during the night times over the monitoring period.

Fig. 10. Temperature decay rates and standard deviation of all the monitored houses, calculated based on the night decay curves.

were investigated. Lowering the thermostat set-point results in shorter preheat time (time from switching the boiler on to achieving the set-point) and therefore lower delivered heating output from the heating system. The energy savings were identified by calculating the reduction of the area under the preheat curve. For the example presented in Fig. 8b the total boiler run time was reduced by 13% for the examined week. This reduction is the result of shorter preheat cycles, less cycling to maintain the thermostat

Prerequisites for this approach to provide reliable results are (i) a consistent heating profile and (ii) low wind speeds during the monitoring period. The first criterion has been confirmed in the previous section, whilst Fig. 9 presents the average hourly wind speeds during the night times over the monitoring period. In total, wind speeds for 8 days are presented as few of the householders activated the temperature sensor 1 day after they received it. The plot shows that prevailing wind speeds were generally low, to the region of 4 m/s or lower. A correlation analysis was performed between the wind speeds and the estimated heat leakage rates during the monitored week; the average Pearson product-moment correlation coefficient found between the two datasets was 0.078, indicating a very weak relationship. This finding was further confirmed with visual observation of the data. The temperature decay rates a and the standard deviation of the temperature decay rates of all the monitored houses were estimated and are shown in Fig. 10. The median a was used as a reference to initially cluster the set of houses into two categories (Thermally tight, Thermally leaky). Using the median standard deviation these were split into a further two categories (Low ventilation variability, High ventilation variability). The total heat transfer coefficient was calculated for all the houses and was used as an indicator to verify the results of the clustering. Table 3 summarises the results for all four categories and gives the basic insulation measures installed in each of the houses (cavity wall insulation and loft insulation). The houses without cavity wall insulation and with low loft insulation have all been correctly grouped in the ‘Thermally leaky’ clusters. Similarly, the houses with high loft insulation and cavity wall insulation have been grouped in the ‘Thermally tight’ clusters. When compared with the overall heat loss coefficient estimated using SAP, both ‘Thermally leaky’ clusters appear to have a higher average heat loss coefficient than the two ‘Thermally tight’ clusters, showing a good agreement between the clustering approach and the actual heat loss coefficient of the building fabric. Houses with similar depth of loft insulation, installed cavity wall insulation and similar heat loss coefficients as calculated in SAP, can be found across all four clusters. The reason for this is that our clustering method is performed based on actual temperature decay rates, which reflect occupants’ behaviour. In contrast, the level of insulation and the overall heat loss coefficient calculated in SAP are static indicators that do not consider the impact of occupants’ behaviour. This highlights the significance of simple everyday actions taken by an occupant, such as opening a window,

Table 3 Classification of monitored houses into one of the four categories and comparison with the total heat transfer coefficient (W/K) calculated using data collected through on-site surveys. LI denotes loft insulation given in depth of insulation installed (mm) and CWI denotes cavity wall insulation. Category 1: ‘Thermally leaky, low variability’

Category 2: ‘Thermally leaky, high variability’

Category 3: ‘Thermally tight, low variability’

Category 4: ‘Thermally tight, high variability’

ID

LI

CWI

W/K

ID

LI

CWI

W/K

ID

LI

CWI

W/K

ID

LI

CWI

W/K

20 18 14 21 13

280 270 250 150 250

No No No Yes Yes

243 297 371 244 340

23 16 15 22 17 19

270 250 250 250 260 260

No Yes Yes Yes Yes Yes

389 267 350 288 314 334

4 7 3 8 1 5 10

280 280 280 260 250 250 250

Yes Yes Yes Yes Yes Yes Yes

197 258 295 335 223 280 217

12 9 2 6 11

295 280 250 250 250

Yes Yes Yes Yes Yes

N/A 254 255 284 320

Average St. deviation

299 51

324 40

258 45

278 27

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Fig. 11. The MyJoulo website (www.myjoulo.com).

Fig. 12. The MyJoulo page that presents the results of our analysis.

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and their impact on a building’s overall heat loss. Another reason is that buildings with similar fabric characteristics were used, resulting in clusters characterised by high level of similarity. 5. A large scale deployment Following our experimental evaluation of the data from the 25 homes presented in this paper we undertook a larger deployment where we developed and deployed a prototype system, named MyJoulo, which both provides personalised home heating advice to households and collects data on typical heating use across a large number of UK homes. The system was launched in beta form on 13th December 2012 and operated until 31st March 2013, surveying 750 users in this time. The MyJoulo system consists of three components: (i) a website (Fig. 11) through which households can sign up to the service and see the results of the data analysis and feedback, (ii) a low-cost USB temperature logger which is sent to the user once they have registered on the website, and (iii) the heating system diagnostics, which are presented to the user through visually appealing infographics (Fig. 12). More specifically, MyJoulo learns a simple thermal model of the home and infers the operational settings of the heating system, i.e. the switch-on and switch-off times, as well as the thermostat setpoint as described in Section 3.1. Then, as per the methodology described earlier in Section 4.1, we calculate the energy savings that would result from making specific interventions in the use of the heating system; specifically, calculating the impact from reducing the set-point either by one degree. The website allows households to request a temperature logger to be mailed to them, upload data recorded by the logger, and view the resulting energy saving advice. Since the temperature logger is returned after use, the marginal cost of providing the service is very low (just postage charges and packaging replacement), and the service can operate at scale at low cost. 6. Discussion The approach described in this paper can be applied when the internal temperature profile exhibits regular heating profiles. In real life more complex heating patterns may arise as a result of more sophisticated programmers that adjust the thermostat setpoint or as a result of regular manual interventions. Various statistical methods based on seasonal maximum and minimum temperatures can be used to estimate the overall energy input over the course of 1 week and model the impact of an intervention. Such methods however, would not allow a detailed tailored feedback on operational changes (such as timer settings). This study has demonstrated a simple method to generate a set of energy diagnostics relating a building’s energy performance and the occupants’ behaviour with respect to space heating and ventilation. The main advantage of the proposed method is that it can address heating issues related to a building’s thermal performance and its occupants’ behaviour at the same time, based on a single field dataset; internal temperature. As a result, the associated complexity and cost are significantly lowered when compared with traditional approaches. The benefits are evident not only for householders but for policymakers, energy providers and insulation installers. Occupants receive feedback about the energy performance of their house and their behaviour, which will enable them to understand better how they use or waste energy in their homes. This approach will help them to associate ‘energy’ with specific actions, such as reducing the central heating thermostat set-point/runtime or decreasing the ventilation.

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The method presented in this work, could also have an impact on schemes such as the ‘Green Deal’. These new schemes often require an assessment by a qualified energy assessor and in some cases the associated cost is substantial enough to deter householders from participating and benefiting. The proposed method can be used as an alternative to on-site energy assessments, making Government initiatives cheaper and therefore more attractive. In addition, this set of diagnostics can be used by policymakers to target and prioritise homes which are more likely to deliver energy savings. It also reduces the risk for the ‘Green Deal’ providers; analysis of user’s behaviour is valuable information to them, in that it is the key factor determining energy and financial savings after interventions. The confidence of the clustering method results is directly related to the diversity and the size of the sample examined, where large and diverse samples are generally considered more suitable. The sample of houses used in this analysis is quite uniform; most of the houses have cavity wall insulation installed and either 250 mm or 260 mm of loft insulation. As previously mentioned, these houses were monitored as part of the ‘‘The role of community-based initiatives in energy saving’’, ESRC project which involved a roll out program of insulation upgrades. A larger and more diverse sample of houses would be more suitable for the approach presented here as the distribution of the temperature decay ratesa and the distribution of its standard deviation would be uniform and wider. In real terms, the impact of occupants’ behaviour would not be as dominant and therefore the resulting clusters would be less homogenous than these shown here. Intuitively, the confidence of the results obtained will increase as the number of houses the model is applied to increases. The extreme cases (poorly insulated and very well insulated) were identified successfully, despite the relatively small and homogenous group of houses examined in this paper. Variability in ventilation rates during the night hours over the monitored period was physically attributed to user’s behaviour. The standard deviation of the thermal transmittance of each cluster indicates that the fourth cluster (‘Thermally tight, high variability’) consists of houses with more similar building fabric than the other three clusters; therefore observed differences in the temperature decay curve can be attributed to human activity. Although direct validation has not been possible, this is the logical conclusion after eliminating any other factors that could have affected the temperature decay rate over the night hours. To further confirm this premise, an experiment with CO2 sensors that will provide the necessary data to validate the temperature decay rate approach is currently considered by the authors. The simplicity of this approach inevitably induces a number of limitations in terms of applicability. The authors suggest that this approach can generate reliable results for homes characterised by simple heating profiles (single set-point and one or two heating periods) controlled by a programmer. In addition, during the monitoring period low wind speeds should prevail to avoid misinterpretation of the infiltration rates. References [1] DUKES. Digest of UK energy statistics 2012, Statistical Press Release; 2012/089. [2] Department for Communities and Local Government. English housing survey housing stock summary statistics tables; 2010. . [3] Palmer J, Cooper I. Great Britain’s housing energy fact file, Department of Energy and Climate Change, URN: 11D/866, London; 2011. [4] BRE. The Government’s standard assessment procedure for energy rating of dwellings, 2009 ed.; 2010. [5] ODPM. The building regulations 2010. Conservation of fuel and power in existing dwellings. Approved document L1B, Office of the Deputy Prime Minister. [6] DECC. The green deal – a summary of the government’s proposals, December 2010, Ref: 10D/996.

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