M. Kordjamshidi, S. King/Energy and Buildings 41 (2009) 125–132

Overcoming problems in house energy ratings in temperate climates: A proposed new rating framework

Maria Kordjamshidi ^a,*, Steve King ^b

^a Faculty of Engineering, Ilam University, Iran

^b Faculty of Built Environment, University of New South Wales, Australia

Keywords:

Energy

Rating

Residential buildings

Energy efficient

Thermal performance assessment

ABSTRACT

This paper first demonstrates that an efficient design for a house in conditioned operation mode differs from that for the same house in the free-running operation mode, and that this is a primary reason for the inability of current energy based rating schemes to adequately assess the performance of passive design in a temperate climate. We examine the Australian Nationwide House Energy Rating Scheme as an example of this problem. A new practical framework for a house rating scheme is then presented. In this proposed framework, the efficiency of a house design is evaluated with reference to its thermal performance in free-running mode, in addition to its projected energy loads in conditioned operation mode. The study uses simulation to evaluate the thermal performances of houses, and employs multiregression analysis to develop the framework. The reliability of proposed framework as compared to the current energy based rating scheme is demonstrated. By attributing more value to the performance of houses in the free-running than the conditioned operation, it is assumed that policy objectives for reducing energy demand for space heating and cooling in the residential building sector are more likely to be achieved.

1. Introduction

Throughout the world, a variety of House Rating Schemes (HRS) has been developed to evaluate residential building performance. While Roulet et al. [ 1] outline multiple criteria and rating methods employed, energy efficiency has been the main parameter in almost all developed building rating schemes. Building rating schemes have historically favoured an ‘energy indicator’ for their performance evaluation (usually normalized by the area or the volume of the dwelling) [2–10], because:

(a) Energy represents a high percentage of the running cost of a building and has a major effect on the thermal and visual comfort of the occupants.

(b) Notwithstanding the likely discrepancies between projected theoretical energy loads and variable real-world consumption, an energy rating can give a potential buyer information on the energy bills that are likely to arise.

(c) Where heating and cooling plants are either a normal, or a mandated part of building construction, energy ratings help to choose appropriate HVAC equipment; and arguably this allows for competition in the market to refine the rated capacity of the size of plant.

(d) In effect this means that houses are assumed to use artificial energy for their space heating and cooling, However, an axiom of sustainable energy use for dwellings has to be that in moderate climates effective passive design can minimize or altogether avoid artificial heating and cooling. Thus, where for instance Australian climates differ from those of Europe and Canada, differences in the programming of HERS were expected [ 11]. Nevertheless, house ratings in Australia, as in other countries, are based on prediction of energy requirements, and have not been modified to give more value to free-running houses; as a consequence, such designs cannot be properly scored through the current Australian rating scheme [12].

Current building rating schemes also generally assume that a house with less energy usage is the most energy efficient house [6]. However, low energy use does not necessarily correlate with energy efficiency [13]; energy minimization is related to the efficiency of appliances as much as it is to the fabric of the building. It has been argued and demonstrated that a simple normalized energy based rating is not sufficient to convey the credibility of an energy efficient design [ 12,14–17 ], and that this issue appears to be more critical when a house is specifically designed to be operated in free-running operation mode [18,19]. Based on such criticisms, authors such as Williamson et al. [20] go so far as to suggest that the Australian Nationwide House Energy Rating Scheme (NatHERS) is a technical and policy failure.

The USA Department of Energy Annual Energy Outlook 2008 with Projections to 2030 [21 ] provides further evidence of the necessity of developing a free-running rating in combination with current energy based rating schemes, pointing to the role of air conditioning in increased demand for higher level of indoor comfort, but also to an identified ‘take back effect’ – which occurs when people with more efficient homes actually use more energy than expected, because they are less cautious about basic efficiency measures such as thermostat settings [22]. It has been noted that despite efforts to improve energy performance, the thermal performance of houses when occupied is generally not as has been expected or intended [23].

Preliminary investigations in this study [ 19,24] comparing the performance of ‘typical’ houses, also confirmed that an effective design for a house for free-running operation mode differs significantly from that for the house in a predominant conditioned operation mode. This alone may be sufficient to explain why in current energy based rating schemes, an efficient free-running designed house appears to be discriminated against in comparison to conditioned houses. The preliminary conclusion reached was that a systematic method for the evaluation of projected thermal performance of a house in free-running operation mode should necessarily differ from that in conditioned operation mode.

The project described in this paper explores methodologies and a framework for assessing a proposed building’s projected thermal performances, in both its free-running and conditioned operation modes. For the purposes of this study, free-running houses were defined narrowly as houses ‘without any mechanical equipment and artificial energy load for space heating and cooling’. Aynsley [25], amongst others, has suggested that this definition is overly restrictive, especially for a warm humid climate. However, the focus of the present study is only temperate climates, because this climate type has the most potential for promoting and using free-running houses.

2. Methodology

In order to develop a framework for HRS based on the performances of a house in its different operation modes, it was required:

· First, to identify the extent to which the thermal performances of houses vary in their different operation modes. In common with most related studies, a simulation method was employed.

· Second, to develop an equivalent rating method for free-running performance, which we refer to as a House Free-Running Rating Scheme (HFRS).

· Third, to propose a robust method to aggregate the two ranking systems of HERS and HFRS, based on simulation of projected performance by the common thermal simulation engine.

2.1. Samples

It is impractical to take into account all different house typologies. Attention was focused on six ‘typical’ detached houses, found by an earlier study to sufficiently encompass the size and planning characteristics of houses most common in the marketplace, in the state of New South Wales, Australia [26]. A general description of the sample houses is given in Table 1.

Table 1

Window, wall, ceiling, floor areas of typical houses.

House	Number	External wall	Window	Ceiling	Internal	Floor
	of floors	area (m^2)	area (m^2)	area (m^2)	wall (m^2)	area (m^2)
1A	1	137	32.4	138.2	96.6	138.2
1C	1	150	24.8	155.4	88.1	155.4
1D	1	196.5	45.9	244.9	160.4	244.9
2A	2	256.7	50	166	156.1	292.8
2C	2	260	56.5	136.3	182.3	315.7
2D	2	234	40	144.4	174.4	229

2.2. Tool

The simulation software used for modeling the houses is AccuRate. A new version of the earlier NatHERS software, AccuRate has been developed as the preferred ‘second generation software’ for the Australian Nationwide House Energy Rating Scheme. The reliability of this software is validated through BESTTEST [27]. AccuRate predicts an annual energy requirement of a simulated house in a standardized conditioned operation mode, and can be run in ‘non-rating’ configuration to predict hourly indoor temperatures in the free-running operation mode. An important factor in the change to the AccuRate software is its capability of considering the effect of physiological cooling by natural ventilation, when computing cooling energy requirements [28].

2.3. Simulations

A total number of 1164 simulations of thermal performance of houses was carried out, for two moderate climates of Sydney and Canberra. This paper only reports results for the Sydney climate. Models were generated from the six ‘typical houses’ and were different from each other in terms of 17 variables (Table 2). Each variant model house was simulated for two different operation modes, conditioned and then free running.

3. Performance evaluation

Evaluation of whether a house is more energy efficient than another house can be complex if the activities in the building, or the climate, or the state of operation of house are not the same. Where the one indicator of efficiency is used, a house designed to

be energy efficient with high-quality performance in its conditioned operation mode might display poor thermal performance in its free-running operation mode.

Table 2

House parameters for simulations.

	Variable	Parameter description (building fabric)	Parametric set
1	X01	Ceiling insulation (resistance)	0, 1, 2, 3, 4
2	X02	Wall insulation (resistance)	0, 1, 1.5, 2,3
3	X03	Floor insulation (resistance)	0, 1, 1.5, 2
4	X04	Internal wall (U value)
5	X05	Infiltration (air change per hour)	0, 1, 2, 5
6	X06	Window covering (resistance)	0, 0.03, 0.055, 0.33
7	X07	Openable window (%)	25 (base), 50%, 75%
8	X08	Shading device (eave length)	0, 450, 600, 1000 mm
9	X09	Orientation (degree)	0, 45, 90, 135, 180, 225, 270, 315
10	X10	Glazing type (shading coefficient)	1, 0.52, 0.70, 0.88, 0.60
11	X11	Roof colour (absorbance)	30%,50%,85%
12	X12	Wall colour (absorbance)	30%,50%,85%
13	X13	Window to wall ratio (N&S) (%)	0 (base)^a, 15%, 25%
14	X14	Window to wall ratio (E&W) (%)	0 (base), 15%, 25%
15	X15	House type	Single storey, double storey
16	X16	House construction	Heavy weight, light weight
17	X17	House plan	6 typical plans

^a 0 (base) refers to the percentage of window to wall ratio in the typical houses which is different for different models. It does not mean that the ratio of window to wall is 0.

Appropriate indicators would be values derived from the parameter that describes the state of a house, dependent on its dominant state (conditioned or free running). The thermal quality of a dwelling can be assessed in terms of estimation of its annual energy requirements in its conditioned operation, or an ‘aggregated annual thermal comfort condition’ in its free-running mode. The first demonstrates the conditioned performance of the house and is used in current house rating schemes. The latter demonstrates the actual performance of the house, and addresses multiple aspects of efficiency in a particular architectural design [ 14]. The thermal performance of a house in conditioned mode is quantified in terms of annual energy requirement (MJ/m²)needed to maintain the occupied zones within a determined temperature band. The thermal performance of a free-running house is quantified in terms of Degree Discomfort Hours (DDH) in occupied zones, taking some account of acclimatization and for variances in relative humidity. These metrics are described in brief below.

When the houses were modelled in the conditioned mode, heating and cooling thermostat settings of 20–24.5 and 18–24.5 °C were applied in the living zone and bed zone respectively. This range coincides with the AccuRate software thermostat settings for mandated ratings for the Sydney climate.

When the house was simulated in free-running operation mode, the indicator to evaluate the thermal performance was Degree Discomfort Hours, a measure of the extent to which the indoor temperature falls outside comfort boundaries. A spreadsheet algorithm was developed to transform annual hourly temperatures into DDH. For this purpose the boundaries of thermal comfort conditions for the Sydney climate were determined using an adaptive thermal comfort model [29,30] expressed in Eq. (1) for 90% occupant acceptability, on a monthly basis.

T_IN_S =0.31T+17.8 (1)

where T is the average monthly (outdoor) temperature (°C).

The boundary of comfort temperatures shown in Fig. 1 is applied for the living zone. The lower boundary of temperature for the bed zone at sleeping time (12 am–6 am) is pulled down by 5 °C because it is assumed that occupants can easily use a blanket. The limit of that band changes on an hourly basis in response to relative humidity. The effect of humidity was accounted for in accordance with the ASHRAE standard Effective Temperature lines, and by employing a simplified equation proposed by Szokolay [31]. Indoor humidity was considered approximately similar to outdoor humidity in free-running houses, although in reality indoor humidity slightly differs from outdoor humidity.

Fig. 1. Thermal neutrality comfort band for free-running houses in the Sydney climate.

4. A comparison of conditioned and free-running thermal performance approaches

Thermal performances of the simulated houses were compared and analyzed, using parametric sensitivity analysis and multivariate regression analysis. The aim of this kind of analysis here has been to investigate the effects of house envelope variations on the annual and seasonal behaviour of the typical houses, and also to investigate possible linkages between the thermal performances of a house in different operation modes in response to the 17 variations described in Table 2. Multivariate regression analysis was then applied to investigate the correlation, similarity and differences between the thermal performance of simulated houses in conditioned and free-running operation modes; and – in order to establish the factors that need to be considered by those attempting to design efficient buildings for different dominant operation modes – to investigate whether the effect of each parameter on the performance of a house is similar or different in both operation modes.

The present study does not explore the interaction effects of design parameters on the thermal performance. It also does not include all occupancy scenarios, although this is a main parameter in evaluating dwelling free-running performance. Both these exclusions are more fully discussed in a previous paper by the authors [24].

4.1. Parametric sensitivity analysis

The parametric sensitivity analysis demonstrated that changing house fabric parameters to improve the thermal performance of a conditioned house (i.e. reduce annual energy load in MJ/m²) does not necessarily improve the thermal performance of that house in its free-running operation mode (assessed based on annual DDH). This result was generally consistent throughout, when one of the 14 variables in construction was added or modified. A part of this analysis is reported more fully in [ 19]. For example the addition of insulation to the external walls of all typical houses resulted in an improvement in the conditioned operation mode, but for single storey houses with heavyweight construction the simulations project a deterioration of annual free-running thermal performance. This is illustrated, for one of the single storey houses, in Fig. 2. Even where the effect on the predicted thermal performances in response to change in an envelope variable was in the same direction, the proportionate improvement or degradation was not the same for both operation modes. For example the addition of R2 insulation to the ceiling of typical houses gave an

average improvement of 42.5% on their predicted conditioned performances; however the improvement on their annual free-running performance was about 32.9%.

The variables were compared with respect to how strongly they affect the thermal performance of the typical base houses. The average relative strength of some of variables is given in Table 3. The main reason for these differences was found to relate to the seasonal performance of the house in different operation modes. For example, in the climates modelled, the addition of insulation in the external wall improves the winter performance of a house and degrades its summer performance, in both free-running and conditioned operation modes. However the improvement in the winter performance of a conditioned house is about 9% greater than that for the same house in the free-running operation. Obviously, in winter, added insulation helps to maintain indoor comfort conditions for longer and therefore reduces the energy requirement for space heating. In comparison it does not make a significant difference in the winter performance of the house in free-running mode, because winter DDH refer to the indoor condition which is below the comfort zone and again, insulated walls tend to maintain the uncomfortable indoor condition for a longer time. The winter performance of the house therefore was not significantly sensitive to the addition of insulation in the external walls in the free-running operation mode. The degradation observed in the overall thermal performance of the typical house in the free-running operation therefore is due to degradation in its summer performance.

These observations provide further evidence that detailed design decisions aiming for an energy efficient conditioned house should differ from those aiming for a free-running house. This is also addressed in the multivariate regression analysis.

4.2. Multivariate regression analysis

The result of this analysis is reported in detail by the authors in [24]. The analysis supported the outcome of parametric sensitivity analysis. Further, it demonstrated the following two key points:

· Correlation coefficients between predicted indicators of annual thermal performances of simulated houses suggest that there is a key difference between characteristic thermal performances of single storey and double storey houses, sufficient to call into question the likely reliability of any system which assesses those two types of houses under a single rating framework.

· Comparing the significance of the variables, according to their beta coefficients, confirms that the contribution of design features or building fabric properties to the improvement of the thermal performance of a house depends on the operation mode. In other words, design for a conditioned house is closely related to its envelope characteristics – those attributes that protect or isolate the building interior from the environmental loads, and maintain indoor thermal comfort conditions with minimum energy consumption to overcome those loads. However, as has long been implied by the alternative terminology ‘climate responsive’, the determinants of free-running performance are more complex.

Fig. 2. Comparison between the effect of wall insulation resistance in prediction of annual thermal performance of a house (D1) in different operation modes.

Table 3

Percentage variations in annual thermal performance of the typical houses in free-running and conditioned modes.

Parameters	Percentage thermal performance variations (Sydney)
	Free running	Conditioned
Ceiling insulation (none to R4.0)	36.4%	46.1%
Wall insulation (none to R4.0)	7.8%	9.7%
Floor insulation (none to R2.0)	4.7%	1.6%
Wall colour (0.30–0.85 absorbance)	3.5%	3.6%
Roof colour (0.30–0.85 absorbance)	3.16%	22.6%
Orientation	6%	5%
Window overhang (none to 1 m)	4.8%	3.7%
Glazing type (SG Clr to SG Refl.)	4.6%	2.1%
Window covering (open W. to Drape)	7.4%	9%
Openable window (0.25–0.75)	6.7%	8.7%
Window to wall ratio (N&S)	1.1%	1.3%
Window to wall ratio (E&W)	0.24%	1.7%
Internal wall (plasterboard to brick)	19.1%	8.2%
Infiltration (0–5 air change per hour)	14.7%	17.8%

.

Therefore, heuristic design decisions inferred from use of a rating tool, to improve the thermal performance of a house intended to be substantially free running, should differ from those for a house intended to be substantially conditioned. It also thereby implies that the regulatory framework for rating free-running houses should be different from that for conditioned houses. Such a framework is developed in the following.

5. Rating thermal performance of houses

A house rating system scores a house by comparing its thermal performance with other houses, which are given the same conditions of climate, and typically user behaviour patterns and house operation. Also typically, rating schemes adopt a rating scale, such as the star categories of the Australian Nationwide House Energy Rating Scheme.

If a single overall rating is to be the objective, it would seem necessary to avoid any unfair treatment relating to house type and house operation mode. Preliminary analysis suggested the following steps in developing a unified rating scheme:

· separating free-running houses from conditioned houses;

· separating house types (in this study double storey and single storey houses);

· determining the score boundaries for each group separately;

· appropriately aggregating the score bands of a house for its free‑
running and conditioned performances, to produce one score.

The choice of procedure used for determining the boundaries of any rating scale varies from place to place, depending on the particular legislation involved in the promotion of energy efficiency. However, five star categories as used until recently in the Australian NatHERS have provided sufficient and meaningful differentiation between the relative efficiencies of different houses in the same condition and operation mode, and are retained in our proposed framework. To determine the score bands for the ratings in this study, discrete efficiency categories were adopted for free-running (HFRS) and conditioned houses (HERS) separately, in each of which five ranges of scores are represented by five stars.

The range of the five categories was defined by‘norm-referenced measurement’ [32].‘Grading on a curve’ was then applied, using the means and standard deviations of the normal distribution of the performance scores. A necessary requirement for application of this method is that the data has a normal distribution. Normal distribution was checked and confirmed for our data.

The star bands derived (based on the mean value and standard deviation of the relevant measure related to operation mode for each of the proposed two design categories) are applicable only for the data and specified climate in this study, and therefore not reproduced here. To implement the proposed framework more widely – say nationally in Australia – a larger sample would have to be simulated, giving rise to modified band boundaries.

6. The combination of the two rating systems

The aim of the integrated rating system is that it will differentiate between the value of an efficient design for a free-running house and one for conditioned house depending on the climate, and national policy objectives for building energy conservation, while not compromising unfairly the value of any efficiently designed house. A suitable algorithm can be employed for aggregating the two discrete rating systems, which gives more value to either an efficient free-running house or an efficient conditioned house. However, this study is concerned specifically with promoting efficient free-running houses. Thus the particular method of aggregation illustrated below aims to give more value to these houses in a moderate climate such as Sydney, as a matter of policy in the context of sustainable development. It is intended to encourage the public to adopt free-running houses in order to reduce energy consumption for space heating and cooling.

A 10 star rating scheme is proposed, using an algorithm to integrate the five star ratings for the same house under HERS (MJ/ m²/year in conditioned mode) and HFRS (annual Degree Discomfort Hours in free-running operation). The algorithm is most conveniently executed as a ‘lookup table’ illustrated at Fig. 3. The eleven cells of the table correspond to the initial range of possible ‘half star’ ratings for conditioned performance. In each cell of the table:

· the first column allocates the probabilistic score for the conditioned performance of a house in a 5 star rating;

· the second column allocates all probabilistic scores for the free-running performance of the house in a 5 star rating;

· the third column allocates the final aggregated score in which: o the top score is obtained from adding the conditioned rating score to the 5 star rating of the house in free-running performance;

o the other final scores allocated in the third column are produced by taking 1 score away from the resulting upper final score. This establishes a different value to the different performances of a house by rewarding the free-running performance of the house.

In this sort of aggregation the final score is mostly less than the score produced from simply adding the scores of both ratings. For instance, in the first group, by adding the first two grades of free-running (5) and conditioned (5) ratings, the result will be 10, which is the top star rating in the proposed new rating system. The addition of the second grades in this group, free running (5) and conditioned (4.5), gives a sum of 9.5, but a score of 9 stars (10-1=9).

A regression analysis was then employed to confirm, and give numerical values to the sensitivity of the final score (as a dependent variable) in relation to the free-running and conditioned scores (as independent variables). The regression obtained confirms the greater dependency of the final score on the free-running performance of a house, as expressed in Eq. (2).

S = 0.40S1 + 0.87S2 (2)

The standardized coefficient for free-running performance is 0.87 where that for the conditioned performance is 0.4. In other words, to produce the final score for the thermal performance of the house, the effect of the free-running score is more than double the effect of the conditioned score. As a result, in order to achieve an acceptable score in the proposed new HRS, a designer would be likely to give priority to free-running design for improving the thermal performance of the house. This supports the objective of the rating framework: to encourage the public and architects to opt for free-running houses. Depending on the appropriate policy for reducing energy consumption, the method could also be applied to give more value to conditioned houses, simply by transforming the method of aggregation of the two ratings.

7. Reliability of the framework

The proposed framework was finally tested for its theoretical sensitivity to improvements in efficient design. A simulated improvement in response to the composite rating was employed. For this purpose the following steps were taken:

· The design quality of the typical houses was improved by modifying design attributes (the 14 parameters in Table 2) in order to enhance, first the free-running performance, then the conditioned performance of the houses.

· The resulting projected annual energy requirements and DDH were input in a regression model to determine the correlation between the indicators of free-running and conditioned performances for these ‘improved’ houses.

· All simulated houses were scored on the basis of the proposed rating scheme to check how their scores on the HERS changed in relation to the changes in their score on the free-running component.

Fig. 3.

The regression showed a strong linear correlation between the annual energy requirements and Degree Discomfort Hours of houses when their thermal performances were improved (Fig. 4).

Fig. 4. Correlation between energy requirements and Degree Discomfort Hours.

The correlation was 0.86 for the Sydney climate and 0.76 for the Canberra climate.

Fig. 4 also clearly shows the relative impact of the improvements, both as lower absolute values (highlighted in the figure by the superimposed boxes) and as changes in the correlations. Before the improvement the correlations were r² = 0.69. After improving thermal behaviour by modifying the building fabric, the correlation was r² = 0.86. This implies that if design quality is improved greatly for either free-running or conditioned operation modes, an

efficient house design can result in good performances for a house in both operation modes.

This implication was then checked against the proposed score bands, to examine the relationship between the scores of houses in free-running (HFRS) and conditioned ratings (HERS). All simulated samples were therefore scored on the basis of the proposed rating framework. The relationship between the scores is shown in Fig. 5.

Fig. 5. Correlation between the scores of houses in free-running and conditioned modes, in the Sydney climate for single story (a) and double storey houses (b).

Fig. 6. Pictorial expression of the proposed framework.

It can be seen that there is a linear relationship between the scores of double storey houses in different modes. However, the relationship would appear to be polynomial amongst the single storey houses. This is evidence of a significant difference between the characteristics of single storey and double storey houses, and confirms our previous conclusion that a rating system would need to separate these two house types for evaluation.

A point worth noting in this approach is that houses with 4 stars in free-running mode get at least 3.5 stars in their conditioned performances, but houses with 4 stars in their conditioned performance do not necessarily score higher than 3 stars in their free-running performance. In other words, an efficient design for a free-running house could improve the performance of that house in the conditioned mode if the HFRS score is not less than 4 stars. This qualification therefore will be included in the proposed prototype framework for HRS.

8. Prerequisites for an efficient design in the proposed HRS

In any rating scheme, a minimum performance level is generally required for designation of efficiency in an architectural design. In Australia, this varies according to the State or Territory and is proposed by authorities in each jurisdiction. For example, the initial regulatory framework of the Nationwide House Energy Rating Scheme in Australia established a maximum of 5 stars computed by the NatHERS software, with 3.5 stars as the prerequisite for an efficient house design. This is now to be superseded by the 10 star system of AccuRate, which requires 5 stars as a prerequisite for an efficient design.

This study proposes an alternative 10 star rating for HRS, derived by aggregating 5 stars for HERS and 5 stars for HFRS. As observed above, a house with 4 stars in free-running mode will achieve 3.5 stars in conditioned operation mode. The combination of 4 star HFRS and 3 star HERS scores 6.5 stars, which is thus defined as the recommended minimum requirement for energy efficient design (Fig. 6).

9. Concluding remarks

The study reported in this paper was initiated in response to long-standing anecdotal criticisms of the Australian Nationwide House Energy Rating Scheme. Architects and other designers have pointed to a perception that it discriminates against the proper application of climate responsive design by not giving sufficient value to free-running performance of houses. Notwithstanding some methodological limitations, by employing conventional analysis of simulated performance, this study has confirmed that in moderate temperate climates, effective strategy for improving energy efficiency design of a house is indeed related to the preferred house operation mode. The findings strongly suggest the necessity of developing a new regulatory framework for such temperate climates.

To create a new house rating framework, an aggregation of two rating systems has been developed. While the use of a simplified

indicator of thermal discomfort (Degree Discomfort Hours) is not new, the study establishes the validity of its use in such a combined rating framework, as an independent rating metric for free-running performance, employing a common thermal performance simulation software.

The proposed new rating framework gives weight to the benefits of deliberate design for both conditioned and free-running performance. For the purposes of this study, its application has been to favour the contribution of free-running performance, but this weighting may be varied at will to respond to different policy priorities in different climates or jurisdictions. It is assumed that if it is employed as a basis for House Ratings Schemes, this approach is likely to deliver significant benefits in terms of reduction in energy demand and increased sustainability.

In deriving the rating measures for thermal comfort and projected dwelling performance, the authors have adopted the adaptive comfort approach. That approach is undergoing further refinement as it is being currently incorporated in ASHRAE standards, and is expected to lead to further improved discrimination between free-running and conditioned performance predictions.

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