Elsevier

Energy and Buildings

Volume 150, 1 September 2017, Pages 307-317
Energy and Buildings

Domestic building fabric performance: Closing the gap between the in situ measured and modelled performance

https://doi.org/10.1016/j.enbuild.2017.06.028Get rights and content

Highlights

  • A ‘performance gap’ is found to exist between measured and modelled building fabric performance.

  • Highly accurate measurement of building fabric properties (i.e. U-values and air permeability) is possible under controlled conditions.

  • Calibration of building energy models using accurate measurements of the building’s fabric properties reduces the observed performance gap.

Abstract

There is a growing body of evidence available to indicate that there is often a discrepancy between the in situ measured thermal performance of a building fabric and the steady-state predicted performance of that fabric, even when the building fabric has been modelled based upon what was actually built. However, much of the work that has been published to date does not fully investigate the validity of the assumptions within the model and whether they fully characterise the building. To investigate this issue, a typical pre-1920’s UK house is modelled in Designbuilder in order to recognise and reduce the gap between modelled and measured energy performance. A model was first built to the specifications of a measured survey of the Salford Energy House, a facility which is housed in a climate controlled chamber. Electric coheating tests were performed to calculate the building’s heat transfer coefficient; a difference of 18.5% was demonstrated between the modelled and measured data, indicating a significant ‘prediction gap’. Accurate measurements of air permeability and U-value were made in-situ; these were found to differ considerably from the standard values used in the initial model. The standard values in the model were modified to reflect these in-situ measurements, resulting in a reduction of the performance gap to 2.4%. This suggests that a better alignment between the modelling and measurement research communities could lead to more accurate models and a better understanding of performance gap issues.

Introduction

There is a growing body of evidence available to indicate that there is often a discrepancy between the in situ measured thermal performance of the building fabric and the steady-state predicted performance of that fabric, even when the building fabric has been modelled based upon what was actually built, rather than the original design intent. In some cases, the measured in situ performance of the building fabric has exceeded the as-built predicted performance of the building fabric by more than 100%. This discrepancy or gap in the thermal performance of the building fabric is commonly referred to as the building fabric thermal ‘performance gap’. If the issues surrounding the ‘performance gap’ are to be addressed, then it is not only imperative that the in situ measurements are accurate, but that the models that are used to predict the performance of the building fabric are also correct and accurate [1]. Otherwise, there is a risk that one of the contributors to the ‘performance gap’ is poor modelling predictions, resulting in a ‘prediction gap’.

Understanding the energy performance of buildings has become an important factor in design and retrofit. With a global aim of reducing the anthropological impact on climate change, recognising how buildings behave is crucial in the analysis of energy and fuel consumption; methods can then be developed in order to mitigate the enormous carbon footprint we have already established. Among international efforts targeting CO2, the UK has pledged to achieve an 80% reduction in CO2 emissions by 2050 against the 1990 baseline [2]. While commercial and industrial sectors are under scrutiny, the domestic sector accounts for 27% of total UK energy consumption [3]. It is in this area that a strong focus is being directed.

UK government legislation addresses decarbonisation by setting out requirements for all new domestic buildings; for instance the introduction of mandatory EPCs in the sale and rental of property, influencing the market towards the occupancy of more energy efficient buildings. Condensing boilers are also a necessity in new buildings [4], which supplement a reduction in heating inefficiency. As dwellings in the UK have traditionally had very slow replacement cycles and long physical lifetimes, attention must also be placed upon the existing housing stock, which is expected to make up at least 70% of the UK’s total housing by 2050 [5]. Realising how these buildings perform for the purpose of retrofitting can provide a pathway to reducing domestic energy consumption.

While the focus of this study will be based upon a typical UK home, it is important to recognise the presence of regulations on a broader scale and the incumbent issues found therein. Casals [6] shows a good example of this by reviewing [7] Directive 2002/91/CE, a European-wide effort to provide the following: a framework for calculating a building’s integrated energy performance, a minimum energy performance requirements for new buildings, minimum energy performance requirements for large scale renovations, energy certification of buildings and a regular assessment of HVAC systems. Casals reveals a stark difference between EU members and their approach to building energy performance, identifying members such as Germany and Denmark who already impose strict measures on the energy performance of their buildings, whilst members such as Spain and France have a much more relaxed attitude to building energy performance. Realised with this directive, as has been found in the UK’s approach to building energy regulations, is the limitation of enforcement for existing buildings, where the focus of energy performance assessment is reserved for new constructions. As has already been established, new constructions will make up a small proportion of the overall building stock over the coming decades, and so the scope for impact of this directive and implementation of any consequential energy regulations is particularly narrow.

Energy use in the home is dominated by space heating, which is the largest single end-use category in the domestic sector, accounting for just over 60% of all of the energy delivered to the existing housing stock in 2011 [8]. A key approach in reducing energy consumption would, therefore, be to look at how heat is lost from the home. The U-value of an element is a measure of the rate of heat transfer through a particular construction. This factor is used to predict the quantity of plane element heat loss through the external elements of a building. U-values are typically calculated using surface composition and depend on a material’s thickness and its thermal conductivity. Discrepancies are often noted in works that compare U-values measured in situ to their theoretical calculated equivalents [9], [10], [11], [12], [13], [14]. Consequently, these discrepancies result in a deviation between energy consumptions predicted by the models and the energy consumption measured in situ. Li et al. [15] discuss the standard assumptions made when using U-values to evaluate the energy performance of buildings. They review work by Baker [16] and Rye and Scott [17], which propose that these U-values are often greatly overestimated. Baker suggests that the limitations associated with using theoretically calculated U-values are typically due to misinterpretation of the true building structure. Environmental factors, such as of wind, moisture and ventilation, coupled with the age of the structure, can also lead to the over-estimation of the U-value. Using a large sample of measured versus modelled U-values, Baker points out that buildings are typically able to perform better thermally in situ than predicted.

Further works compare the calculated U-value of a building element to its operational U-value, where the scale of discrepancy was found to vary across a wide range. Gaspar et al. [18] for instance investigate the theoretical and actual thermal performance of three façades of different composition. In using both the average method and the dynamic method of ISO 9869-1:2014, they found that results for two of their façades gave a reasonable difference between the theoretical and actual U-value (not greater than ±5% for the average method and not greater than ±1% for the dynamic method). A third façade tested however, demonstrated a difference of more than ±20% when using the average method, which was reduced to −9.6% when using the dynamic method. The larger discrepancy was largely accredited to poor environmental conditions, and although discrepancy was highlighted in each case, the remedy of applying the dynamic method over the average method is also highlighted in their work.

Evangelisti et al. [19] had previously investigated the difference between calculated and measured U-values in a similar study. Again placing three different façade permutations under scrutiny, thermal transmittance was measured using the average method of ISO 9869. EN ISO 6946 was used to calculate the theoretical transmittance in each case and then used for comparison against the measured value. Their work revealed a staggering discrepancy, in particular for the older building of the three which demonstrated a difference of +153%; this goes to show how older buildings could be earmarked for extensive retrofit based upon the assumptions of poor thermal performance, when in reality they massively outperform expectations. In a reverse of this, a different façade (and its composite layers) are found to perform considerably worse than predicted, with an average difference of −37%, thus revealing the benefit of overly pessimistic assumed U-values. Evangelisti et al. identify that a key issue in calculating the U-values for comparison is the non-invasive nature of their approach, in that they cannot identify homogeneity of their wall structures; assumptions of the façade composition therefore, could be incorrect. This demonstrates how − in the field − it is possible to be faced with the kind of problem where non-invasive measurement is required and where little or no information is available regarding wall structure and composition. Assumptions therefore have to be made when there is every possibility that the structure will differ physically (perhaps an additional material is inserted within), which could be detrimental to the results. In this case, modelling the older building from this study using the U-values calculated from the assumptions made would generate a grossly inaccurate review of the building’s thermal performance.

To overcome the downfalls of non-invasive investigations of a building’s envelope fabric, the ‘destructive method’ can be used in which a hole is bored from the outer envelope using a hollow drill; the wall composition is then analysed for accurate measurement of its thermal properties. Desogus et al. [20] used such a technique in a comparison of non-invasive and destructive methods for analysing the thermal properties of a particular building’s envelope. Their study discovered that although the benefit of knowing the full composition of the wall in question, greater uncertainty was attributed to the destructive method than with the non-invasive method, which used the average method of ISO 9869. The higher uncertainty was accredited to the inability to fully recognise the construction materials taken from the destructive method, with further difficulty arising when analysing material density and moisture content. An additional revelation from the study concerned the temperature gradient across the element. Desogus et al. chose to establish two distinct temperature differences of 7 °C and 10 °C, finding that the higher temperature difference gave a lower uncertainty, and is most likely due to the increase in mono-directional heat flow.

Situational discrepancy in U-values can be considered in the modelling of existing retrofit buildings. Fitton [21] looks at energy monitoring in retrofit projects, identifying a performance gap between the measured building performance and its modelled counterpart. High levels of discrepancies are seen in a significant proportion of dwellings when considering post-retrofit energy performance against the modelled predictions. Tronchin and Fabbri [22] previously comment on this energy performance gap, accrediting inconsistencies to varying archetypes and unpredictability in Mediterranean climate conditions. As similar unpredictability is expected in the UK climate due to global warming, this additional effect may be another cause for concern. Therefore, a high probability for misinterpreting the building fabric thermal performance in retrofit buildings can be expected, leading to an under or over estimation of its benefit.

The impacts of using overly pessimistic U-values can be far reaching, as discussed by Ahern et al. [23]; primarily that buildings are assigned an energy performance rating lower than they deserve and, potentially, buildings could be earmarked for costly and unnecessary retrofit. In their study, they investigate how buildings in Ireland are identified for performance assessment. Without additional data, the archetype and construction period alone are used to determine a default U-value. EPCs for these buildings are then automatically generated in software − DEAP [24]. Incorrect EPCs would lead to poor predictions of building performance, potentially acting as a trigger to instigate unneeded retrofit. The pessimistic rating of dwellings did in fact lead to a rapid period of retrofit in Ireland [25]. The knock-on effect of this is that the validity of EPC ratings in Ireland has been questioned, and as a consequence Ahern, Norton and Enright propose that a reconsideration of EPCs is required. While the UK method of approach for EPC accreditation of RDSAP employs more data, such as structure, dimensions, heating system type, levels of insulation and fenestration/lighting types, it can be expected that the default U-values incorporated within the software, though likely to be more realistic, will still incline towards the pessimistic.

Housez et al. [26] are among authors of works which explore the projected and actual energy performance of buildings post-retrofit. Their study looks at seven buildings situated in Austria which have received varying depths of retrofit in an attempt to improve on thermal performance and reduce energy consumption. Varied methods were found to be used in the preparation of the original energy performance certificates, which presented enough uncertainty to warrant the authors generating their own predictions for energy consumption. In all but one case, a huge discrepancy was found between the predicted energy consumption of each building vs the actual energy consumption of the same buildings (by up to a factor of 6). The authors, recognising this performance gap, sought to investigate the variance of three factors within their predictive calculations: localised weather profiles to more accurately model for the realistic weather conditions of the building; air change rates due to increased ventilation behaviour; and the maintenance of internal air temperatures, which were measured on site. By applying corrections to their predictions using these factors a much reduced performance gap was observed, and indeed a review of occupant behaviour did show that increased ventilation accounted for a majority of the original discrepancy.

While Housez et al. [26] show how discrepancy in predicted performance of retrofit buildings can be effected by occupant behaviour and the presence of excess ventilation, Gupta and Gregg [27] demonstrate how, by investigating two different buildings post-retrofit, discrepancies in the anticipated fabric performance contributes to a performance gap not only due to a varied thermal performance, but due to the occupant behaviour also. Their study took two archetypes into account − an older pre 1920’s Victorian house and a more modern house. Discrepancies in the post-retrofit U-values were found on individual building elements (some higher than expected, some lower), however this meant the actual global building U-value met the as predicted value. In contrast to this, the actual U-values of the post-retrofit modern house were found to be higher than anticipated. In both cases however, the predicted (target) consumption of energy was found to be lower than the actual value. Some explanation as to why this is lies with the discrepancy of building fabric thermal values, however the impact of occupant behaviour (specifically the ‘pre-bound’ effect) is also explored − where occupants consume less energy for space heating than predicted pre-retrofit, anticipating established poor thermal performance and the inability of the building to retain heat. The two studies demonstrate how both occupant behaviour and inaccurate assumed U-values and air changes can lead to considerable performance gaps and poor predictions of energy consumption with retrofit projects in particular; calling for some means of correction the predictive models, such as the steps taken by Housez et al. [26].

These observations are summarised well by de Wilde [28] in a review of the core reasons behind the performance gap and the implications thereof. Three main types of performance gap are identified in this work, which are predictions vs. measurement, machine learning vs. measurement, and prediction vs. displayed energy performance. Root causes of the gap are attributed to a large number of factors: misalignment of design for purpose and actual purpose, efficient yet complex devices, poor construction practice, a lack of constructive feedback, and general human behaviour such as the building culture and lack of client education on building performance. Conversely, de Wilde discusses an effort to try and close this gap; education of performance gap issues, improved monitoring and measurement techniques, adapting the construction culture, and the improvement of predictive models are all ways in which researchers are actively attempting to close the gap. This review supports the recommendation made in this paper that closer collaboration, not only between measurement and modelling communities, but also within construction is needed.

The method of more accurately modelling the building fabric thermal performance of a building is designated as ‘calibration’ in this paper. Manfren et al. [29] suggest that energy performance modelling is merely the simplification of complex physical procedures resulting from the periodic replenishment of energy. They note that as simulation tools become more sophisticated, additional system parameters may be modelled; however, as each parameter is attributed to a specific uncertainty, combined uncertainty grows with model intricacy. They go on to introduce regression techniques in order to improve accuracy in model predictions. In particular, Bayesian analysis is applied as a probabilistic method for anticipating uncertainty of inputs and outputs. The framework as developed by Kennedy and O’Hagan [30] looks at likelihoods of subsequent probabilities in order to reduce these uncertainties. Bayesian analysis is supported as a calibration method in a number of other works, for example Heo et al. [31] and Tian et al. [32], [33].

A similar method for reducing uncertainties in energy performance modelling looks at deterministic calibration. Pan et al. [34] use this method, refining their model by repeatedly varying factors of uncertainty until a closer match is ascertained. While this method demonstrates superiority in uncertainty modelling, it proves to be time consuming in nature.

An alternative calibration method involves manual adaptation of the created software model until the output metrics match those of the in situ measured data as seen in work by Marini et al. [35]. In their work, the Designbuilder software was used to develop a base model from a real domestic building located in Loughborough in the UK. Using a network of sensors, in-situ data was collected in order to alter their model over several stages of calibration. Weather data, HVAC operation, infiltration rates and heat flow were measured and used to replace the built-in data contained within the software and a comparison of energy consumption was made against the original model at each calibration step. The study found that significant reductions in prediction error gap from could be achieved by applying these calibrations. The remaining error is attributed to the model’s inability to accurately replicate the true behaviour of the heating system, for example the dynamic behaviour of supply heat timing and the system’s response to peaks in consumption.

Work comparing the process of both automatically calibrating a building energy model with that of manual calibration is explored by Chaudhary et al. [36]. In their study, numerous parameters within the model − classified under material, people, lighting, electrics and HVAC − are purposefully sabotaged; the authors are then tasked with regenerating an accurate model. A trade-off between accuracy of the final models and the time taken to achieve these models is noted. Importantly that the automatic process of calibration took much less time, but the manual process delivered a marginally better accuracy.

The work in this paper uses the manual adaptation method of calibration using the unique Energy House facility at the University of Salford. While techniques using automatic calibration with some function for optimisation are a more efficient way of closing the performance gap, as shown by Mihai and Zmeureanu [37], the logical correction of input parameters more closely matches the model to the building with a successful reduction in the performance gap.

In this facility, a typical pre-1920’s UK home has been built using largely reclaimed materials inside a climate controlled chamber. Details of this test facility are contained within Ji et al. [38]. Instead of simply measuring impact factors that might attribute variation in energy consumption, such as weather, temperature and system performance, it is proposed that these are all controlled. By creating a steady-state environment, it is possible to accurately measure important building characteristics such as U-value and infiltration rates and compare these to the steady-state default values that are used as input into the software models. In accordance with the work undertaken by Marini et al. [35], the measured in situ data can then be used to modify the software model of the Energy House (EH) and thus produce a model that, in theory, more accurately reflects reality. By controlling all variables, this study also eliminates uncertainty in system performance as observed in the Marini study, moving for more accurate predictions of energy consumption. The intended outcome of this study therefore, will be a reduction of the ‘prediction gap’, and ultimately in the building fabric thermal ‘performance gap’.

Section snippets

Method

A measured building survey of the Energy House was carried out to produce a scaled representation of its structural elements. Floorplans of each level of the building are shown in Fig. 1a and b, with an external shot of the building in Fig. 2. Using the floorplans from this survey, an accurate model was built in Designbuilder. This software performs dynamic energy simulations and is a front end user interface for EnergyPlus. It has been chosen for this study due to its prolific use in

Results and discussion

Data will be initially compared between the standard model and the coheating test. To understand any gaps in performance, the standard values of air permeability and U-value will then be compared to those measured in-situ. After adjusting these factors, the measured data is to be compared with the calibrated model and implications discussed.

Conclusion

By building a model to typical standards, a performance gap has been shown to exist between measured and modelled energy performance. From a standard model it has been possible to demonstrate that, through several stages of in situ measurement calibration, it is possible to close the performance gap between measured and modelled data. Substitution of in-situ measurements of air permeability and U-value have been used to reduce this gap considerably. A discrepancy in the overall heat flux for

Funding

This research was funded by the School of Built Environment at the University of Salford.

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