Experimental validation of a EnergyPlus model: Application of a multi-storey naturally ventilated double skin façade

Accepted Manuscript Title: EXPERIMENTAL VALIDATION OF A ENERGYPLUS MODEL: APPLICATION OF A MULTI-STOREY NATURALLY VENTILATED DOUBLE SKIN FAc¸ADE – Author: Aleksandar S. Andelkovi´ c Igor Mujan Stojanka Daki´c PII: DOI: Reference:

S0378-7788(16)30103-7 http://dx.doi.org/doi:10.1016/j.enbuild.2016.02.045 ENB 6462

To appear in:

ENB

Received date: Revised date: Accepted date:

25-12-2014 19-2-2016 22-2-2016

Please cite this article as: Aleksandar S.AnDF elkovi´c, Igor Mujan, Stojanka Daki´c, EXPERIMENTAL VALIDATION OF A ENERGYPLUS MODEL: APPLICATION OF A MULTI-STOREY NATURALLY VENTILATED DOUBLE SKIN FAc¸ADE, Energy and Buildings http://dx.doi.org/10.1016/j.enbuild.2016.02.045 This is a PDF file of an unedited manuscript that has been accepted for publication. As a service to our customers we are providing this early version of the manuscript. The manuscript will undergo copyediting, typesetting, and review of the resulting proof before it is published in its final form. Please note that during the production process errors may be discovered which could affect the content, and all legal disclaimers that apply to the journal pertain.

EXPERIMENTAL VALIDATION OF A ENERGYPLUS MODEL: APPLICATION OF A MULTI-STOREY NATURALLY VENTILATED DOUBLE SKIN FAÇADE

Aleksandar S. Anđelkovića* *Corresponding author e-mail: [email protected] a

University of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovića 6, 21000

Novi Sad, Serbia

Igor Mujana a

University of Novi Sad, Faculty of Technical Sciences, Trg Dositeja Obradovića 6, 21000

Novi Sad, Serbia

Stojanka Dakića b

University of Novi Sad, Faculty of Economics, Segedinski put 9-11, 24000 Subotica, Serbia

Highlights 

A detailed measurement and model calibration is done in order to validate proposed simulation model of a naturally ventilated double skin facade



For each regime (winter, transitional and summer) and for temperature and air velocity validation process was done



The research results highlight a very good agreement and a high level of matching the measured and simulated

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ABSTRACT Nowadays, energy simulation in many respects assists researchers and engineers in the design phase, as well as in the buildings operational phase. Reliability of the integrated simulation model and the knowledge and experience of the user are the cornerstones of good quality simulation prediction result. This study was preceded by a detailed and long-term experimental analysis of the building with a double-skin ventilated façade [10]. The paper presents results of the measurement, fine-tuning process and validation of the numerical simulation model. The simulation software tool, EnergyPlus in combination with Airflow Network Algorithm algorithm was used for the cavity air temperature and velocity prediction. When the model was verified, the criterion of eligibility were defined with the recommended statistical indicators. In order to achieve the better assessment, the validation process was done trifold: for winter, transitional and summer season. Overall, the research results highlight a very good agreement and a high level of matching between measured values and simulated results, which is a confirmation of the applied energy simulation tool. KEYWORDS Multi-storey office building; Double skin facade; Energy Plus; Validation; Statistical indicators;

NOMENCLATURE DSF

double skin facade

WWR

window to wall ratio

HVAC

heating, ventilating and air-conditioning system

MBE

mean bias error

RMSE

root mean squared error

CV(RMSE)

coefficient of variation of the root mean squared error 2

R2

coefficient of determination

NE

north-east

W

winter

S

summer

T

transitional

Σ

total

1. INTRODUCTION In case of the double skin facade (DSF) concept, energy consumption represents the most important parameter, nevertheless in the designing process there are many other considerations like: natural ventilation, lighting, noise or wind protection, etc. Therefore, DSF envelope concept requires a comprehensive engineering approach. Other considerations that were previously mentioned, in most cases lead to an improved quality of the indoor comfort. This can be a major contribution to "non-energy" benefits, such as the occupant’s health. Nowadays, in order to quantify and investigate energy performance of DSF, many authors are using various total building simulation tools (e.g., EnergyPlus, TRNSYS, ESP-r, BSim, TAS, etc.). The results of the some of these studies relied only on simulation quality without any measurement validation [1, 2 and 3]. These results can led to the wrong conclusions and concept opinions. Furthermore, a very important issue is the simulation tool accuracy [4]. The report [5], of the experimental validation of building simulation tools for the DSFs, showed that none of the observed models offered continuously accurate results. Conclusion was that models were crude and that they needed further improvements and fine tuning. Recently published papers [6, 7, 8 and 9] prove positive results and conclusions of the high level of accuracy in case of validation of DSF simulation models. This is the best possible way to clear scepticism and

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increase quality of the simulation models. The quality of the validation results is essential for the concept assessment and for further energy saving estimations that can be achieved by these reliable models. Thus far, many researchers have used various methods for DSF modelling [24], such as: analytical and lumped models, non-dimensional analysis, airflow network modelling, control volume approach, zonal approach, numerical solution of partial differential equations, computational fluid dynamics (CFD) and integration between building energy and airflow models. When selecting the method for DSF modelling, attention should be primarily given to the results that are expected to be achieved. This refers to the expected level of accuracy of the results, the time required for the simulation run, and the complexity of the model and the level of knowledge of the future users.

2. METHODOLOGY For this research, previously obtained experimental results [10] were used in order to validate simulation model created in software tool EnergyPlus 8.2 [11, 12] combined with Airflow Network Algorithm [13]. Overall research process and algorithm are described in Fig 1. Model validation is a phase that supersedes model fine-tuning. The validation process should quantify the accuracy of results obtained by simulation and compare them to the results obtained through measurements. Statistical methods and their indicators are most commonly used to assess the accuracy level of simulation model results. Very important remark is that validation process cannot be just one time activity. Comparatively, for the highest level of reliability, validation process needs to be conducted on a continuous basis. Accordingly, this research will provide triple validation process in three seasons: winter, transitional and summer. Currently, in the case of predicting temperature and air flow, guidelines are not available and

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there is no standard which can provide a procedure nor statistical accuracy level indicators. Therefore, for this validation process recommendations from [14, 15 and 16] are used. The following statistical indicators will be used for assessing the level of simulation model accuracy: 1. MBE (Mean Bias Error) MBE =

∑ni=1(Si − Mi ) (K) n−1

2. RMSE (Root Mean Squared Error) ∑ni=1(Si − Mi )2 (K) n−1

RMSE = √

3. CV(RMSE) (Coefficient of Variation of the Root Mean Squared Error) CV(RMSE) =

RMSE N

∙ 100 (%)

4. R2 (Coefficient of Determination) 2

(n ∑ni=1(Mi Si ) − (∑ni=1 Mi )(∑ni=1 Si )) R =( ) ∙ 100 (%) (n(∑ni=1(Mi2 ) − ∑ni=1(Mi2 ))(n(∑ni=1(Si2 ) − ∑ni=1(Si2 )) 2

In methods 1 and 2, n is replaced with n-1 (number of degree of freedom). This is done in cases when the number of measurements and simulations is limited, i.e. not infinite. Degrees of freedom originate from the geometric representation of the problem associated with mean relative and mean square error. In this case, the number of degrees of freedom is equal to the number of dimensions of the geometric space prepared for problem solving. If m parameters are estimated for a particular source of change based on n independent measurements or simulations, the degrees of freedom equal m-n. Given the fact that results of measurements and simulations are estimated based on their arithmetic mean (m=1), the degrees of freedom are equal to n-1.

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Aforementioned recommendations and indicators are suitable for energy analysis of whole structures, in cases when values are constantly positive (HVAC equipment energy consumption). When it comes to temperature or airflow predication, no recommendations have been provided so far. Despite the absence of recommendations, these indicators are the most frequently used and most suitable techniques for validating simulation models. In addition to the four proposed indicators, two more will be introduced into the analysis. These are:

1. DMIN DMIN = min(Si − Mi ) (K) 2. DMAX DMAX = max(Si − Mi ) (K) where: Mi– measured value at one point; Si – value obtained by simulation; n – total number of measurements; N – arithmetic value/mean of measured data, N =

∑n i=1(Mi ) n

.

MBE is a statistical indicator that indicates the average deviation of values predicted in the model from actual (measured) values of the observed phenomenon, that is, the tendency of deviation of one set of data from the others. A positive value of this indicator would suggest that the model over-predicts values (estimated values are greater than actual/measured values), while a negative value would suggest that the model under-predicts the value of the observed phenomenon. A low value of MBE is desired. The ideal value of this indicator would be zero, as that would suggest that there are no differences between values of the observed phenomena predicted in the model and actual, measured values. This indicator cannot take into account the error, when MBE is decreased due to both positive and negative values of the difference (S-M). 6

The RSME indicator is often used to assess the difference between results obtained through computer simulation and values obtained by measurement. RMSE indicates how certain data series vary from other data series. This indicator suggests the average mean deviation (error) and the degree of data variation, but at the same time does not provide any explicit information on the relative magnitude of the average difference between the predicted and the and recorded value. This indicator provides information of model performance over a certain period of time. The value of this indicator is always positive, while the ideal value of RMSE is zero. Coefficient of variation of the root mean squared error (CV(RMSE)) is an indicator of the relation between RMSE and the arithmetic mean. It is most commonly expressed in per cents, and suggests the share of RMSE in the arithmetic mean. The square of correlation coefficient, also known as coefficient of determination is a relation between covariance divided by predicted and recorded values of the observed phenomenon. It is a measure of the predictive capability of a regression model. Determination coefficient indicates the share of explained variance in the total variance. As an indicator of quality of regression, it gives answer to which part of variation of the dependent variable (simulation results) is explained by the model and which is explained by variations of the independent variable (measurement results). If the correlation coefficient is R=0.8, determination coefficient R2 equals to 0.64; this would suggest that the share of explained variance of observed phenomena in the overall variance is 64%. In other words, 64% of results obtained by model simulation match the measured values. The main question in model validation is when a model can be considered validated? The answer to this question is based on generally accepted and recommended criteria, the so-called acceptance criteria. Based on mentioned standards and guidelines, the recommended borderline value of R2 statistical parameter for measurements and simulations conducted hourly is given as follows:

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1. R2≥75% Since no recommendations have been made for other indicators, their analysis will be based on personal estimates. This assessment will primarily be based on the level of accuracy and consistency of results obtained by simulations with those obtained by measurement. Such structured and validated model will subsequently be used for the assessment of a suggested seasonal operational strategies in order to quantify the effects of energy savings associated with the proposed measures. Also, these validated models can be used for comparative energy analysis between DSF and traditional facades. Table 1 presents an overview of results obtained by various authors who applied verification of simulation models of buildings with DSF. The results refer to average and maximum deviations (temperature and flow velocity), as well as the values of coefficient of determination R2. By examining the above mentioned sources, it can be concluded that average errors are below 5°C, as well as that the value of errors decreases as conditions are better controlled (the higher the influence of natural flow, the greater the errors). In addition, it is logical that error values are lower in cases where more detailed measurements are conducted, more accurate input parameters are presented (material characteristics, method of use, etc.) or with fine-tuned simulations with sensitivity analysis for key parameters.

3. MODELLING AND SIMULATION 3.1 Test multi-storey building The test office building was VIG Plaza, located in Belgrade, Serbia (latitude 44.5°N, longitude 20.3°E, +1h GMT). Climatic conditions in Serbia can be described as moderate-continental with more or less pronounced local characteristics. The hottest month is July, where temperatures can reach up to 42°C whereas the coldest month is January, with minimum temperatures around -20°C. Average annual, winter and summer air temperatures for the period 8

1961-1990 for the area with altitude of up to 300 m are 10.9, 4.9 and 23.2°C respectively. Transitional season is represented by the spring period, which is characterised by moderate outside conditions with a couple of extreme winter or summer intervals. Annual sums of solar radiation duration are in the range of 1,500 to 2,200 hours with maximum intensity of 1,000 W/m2. During the warmer part of the year winds from W and NW are predominant with average speed from 1 to 3 m/s. In the cold period, winds from E and SE dominate with average speed from 5 to 11 m/s. The building was constructed in 2011 and it is first example of a multi-storey object with DSF in Serbia. Its design represents one of the unique types of DSF. The first facade layer is made in the traditional manner (with transparent and opaque part, WWR is 45%) and additional second layer that is comprised completely out of glass. In most cases, both layers of DSF for office buildings are transparent. Fig. 2 shows the layout of target building, its surroundings and selected part of the building envelope which were used for EnergyPlus model. Usable net building area used for EnergyPlus modelling was 1,048 m2. Important remark is that motorized ventilation inlet and outlet dampers are not installed at the bottom and top of DSF. As a result, the facade is opened all the time and cannot be regulated to current outside conditions. All other information about building dimensions, material properties, details about exiting HVAC systems, experiment setup and measurement results can be found in [10].

3.2 EnergyPlus + Airflow Network Algorithm model Inception of building model with DSF refers to its graphical representation. The research topic is the modelling of the building covered with DSF, thus this part be well analysed and presented in detail. The interior (zoning, HVAC systems, heat gains, etc.) will be shown to the extent that satisfies the design and the current operating conditions of the observed building. Part of the building's envelope with DSF, where the measurements were conducted, were used for the

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formation of EnergyPlus simulation model. Orientation of the selected part of the building's envelope is oriented to NE. Due to the availability of measuring equipment and simplification of the model, the facade is divided into three zones (lower (1st and 2nd floor), middle (2nd and 3rd floor) and upper (4th and 5th floor) zone). Sophisticated modular software ''Design Builder" version 4.2 was used to form a physical model as a part of the existing building with DSF. This allowed for a precise, detailed and relatively quick formation of the desired model. Software's graphical environment is user-friendly, while the program's ''engine'' is EnergyPlus version 8.2. Hence, the generation of model that completely meets all the constructive dimensions and thermo-physical properties of all materials used in existing building was fully enabled (Fig 3). Also in the program's environment, all zones are created corresponding to the existing HVAC systems together with their design and operating conditions during the current exploitation of the object. Furthermore, detailed analyses was carried out directly in the EnergyPlus since ''Design Builder" has the ability to export and generate .idf files. In terms of making a model which would be a complete virtual representation of the real building, all available data and information about the building and its HVAC system was used in the process [10]. To achieve better accuracy, in the process of validating the model, the following data was used: - exact dimensions of all constructive layers and other parts of the building; - available thermo-physical properties of the all materials used in the building; - all information about existing HVAC systems; - design and operating inside temperature; - the number of occupants per room, the exact effects of the internal equipment and lighting systems; - accurate data on the levels of shadowing and the amount of infiltration; 10

- existing user schedule of the facility; - data on wind pressure coefficient (cp) was obtained through "Gp generator" for the real configuration of the observed part of the building envelope with DSF; - generated calibrated meteorological year formed from measurement data. To fine-tune the model, parametric studies, sensitivity analysis and experience from similar models [4, 6, 7 and 9], were used. Table 2 shows details about used EnergyPlus and Airflow Network Algorithm simulation parameters.

4. VALIDATION As was stated, there is a deficiency of the recommendations or standards when it comes to validation of the air temperature and air velocity prediction model. Standard statistical indicators, such as MBE, RMSE, CV (RMSE) and R2 can provide a sufficiently accurate picture of the quality of the performed simulations. To increase the credibility of the assessment, the process of validation will be done in three regimes (winter, transient and summer). In this sense, the results of the following parameters are checked for all façade zones: 1. surface temperature of the inner surface of the outer DSF layer; 2. surface temperature of the outer surface of the window of the inner DSF layer; 3. surface temperature of the outer part of the parapet of the inner DSF layer; 4. air temperature in the DSF cavity; 5. air velocity in the DSF cavity. As an example, Fig. 4, shows difference between measured and simulated (air temperature in the DSF cavity).

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4.1 Validation results Based on the conducted quantitative statistical analysis, indicators were obtained are of the quality of the set of simulation results, which are presented in the Table 3 for the inside surface temperatures of the outer DSF layer. The coefficient of determination (R2) indicates a high degree of explanation of the simulation model variation in each case (zone/regime). In other words, over 90% of the results obtained by the simulation model coincide with the measured values. The best results of this indicator were recorded during the summer in the third zone (96%), and the worst were recorded in the first zone, the winter regime (93%). Bearing in mind the set standard in this field by which the value of the statistical parameter determination coefficient greater than or equal to 75% is considered desirable, set model can be characterized as a quality model in terms of prediction. Medium bias error parameter (MBE) shows the prevailing negative value of the average deviation between the model prediction and measured values of the observed phenomena. This indicates that the model predicts below the actual value. In regards to the aforementioned, the observed indicator does not take into account for the error of neutralising of the positive and negative value difference obtained by simulation (S) and measurement (M). Values should be calculated at slightly lower mean bias error values from the actual. Having these statements in mind, the parameter results are better if its values are closer to zero. The best MBE indicator values were obtained for zone III, in winter regime (-0.15), while the worst values were recorded for zone II, in summer regime (-1.25). The root-mean-square deviation (RMSE), which simulates the actual values obtained from the phenomena, is relatively low and points to small data series fluctuations obtained by simulation in relation to a series of data recorded in the building by measurement. The most preferred values are those that are closer to zero. In this sense, the best value of this indicator recorded in

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the spring in the first zone (1.62) and worst in the summer, Zone III (2.39). The coefficient of variation (CV (RMSE)), as an important indicator of the representativeness of the sample and the reliability of the experimental methods used in this study, is in the normal range from 7% to 15%. Therefore, the reliability of the simulation results cannot be brought into question. The best indicator values of the coefficient of variation were achieved in the course of the summer for the first zone (7.01%), and the worst results were recorded in winter regime (14.25%) - I zone. The minimum difference DMIN (S-M) and maximum difference DMAX (S-M) indicators are in synergy with other indicators and add to the claim that the simulation model was well-defined and calibrated. The minimum values of the difference between the simulated and measured results are very close to zero, while maximum values are ranging up to -7.2 in case of III zone summer regime. Maximum differences are of no concern since they occur rarely and only in few hours during the observed period. Analysis with indicators model validation of the model on the example of the temperature of the outer surface of the window surface temperatures of the inner DSF layer indicates very similar results as in the previous case (Table 4). The coefficient of determination (R2) is sufficiently high for all three zones and modes, with the higher values in the third zone. In each single case, the percentage of explained variability in the total variability exceeds 91% and points to a well simulated model. The best values are achieved in zone III during the transitional regime (96) and worst in the first zone, the winter regime (91). In addition, it is important to specify the low value of MBE and RSME indicators which individually indicate good agreement of data, good flexibility of simulation model and thereby small simulation deviations between simulated (S) and measured (M) values. MBE indicator values range from -0.11 (Zone II, winter regime) to -1.66 (III zone, summer regime). As for the value of the indicator RSME, its values range from 1.6 (Zone II, transient regime) to 2.69 (III

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zone, summer regime). When it comes to the temperature of the outer surface of the DSF base layer window, the coefficient of variation ranges from 6.49% to 11.48%, and this interval is still deemed acceptable in terms of control of the representativeness of the sample. Indicator DMIN (S-M) converges to zero and indicates a good match between simulation and measured values. On the other hand, by observing the indicator DMAX (S-M), positive value of the difference (S-M) can be observed in the winter regime in all three zones which points to the fact that the values obtained in this segment by simulation were higher than the real values. Maximum values range up to 6.93 and these values do not represent a danger to the confirmation of the model, since they occur rarely, only in a couple of hours during the reporting interval. In the case of the analysis of the calculated indicators of confirmation model for the surface temperature of the outer part of the base layer DSF parapet, the obtained results indicate a high level of accuracy as in previous cases (Table 5). The values of the coefficient of determination (R2) are reasonably high for all three zones and regimes with somewhat higher values in the third zone in all three regimes. In each individual case the percentage of explained variability in the total variability exceeds 83% and refers to the correctly defined simulation model. Low values of statistical indicators MBE and RSME complete assertion that the model is confirmed in high degree and that the results obtained individually indicate good agreement of data, good flexibility of simulation model and thereby obtaining small deviations in simulation (S) and measurement (M) values. MBE indicator values range from 0.08 (Zone II, transient regime) to 1.98 (I zone, winter regime). As for the value of the indicator RSME, its values range from 1.77 (I zone, summer regime) to 2.68 (III zone, summer regime). The coefficient of variation CVRSME in the case of model confirmation for the surface temperature of the outer part of the base layer DSF parapet, are in the range of 6.18% (III zone,

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summer regime) to 19.54% (I zone, winter regime). These values are still within the limits of eligibility in terms of control of the representativeness of the sample. In the observed case, an indication of DMIN (S-M) converges to zero as the ideal value, while the indicator DMAX (S-M) takes high positive values for all three zones during the winter, negative during summer and in the spring the value of this indicator ranging from high positive 6.54 difference in zone one, a negative difference of -5.66 in the third zone. The values of the maximum difference still do not present a risk to the quality of the model, because these values rarely appear, only a few hours during the whole interval. Indicators that assess the level of accuracy of the simulation model results on example of DSF air temperature show much similarities with the same indicators related to other cases analysed (Table 6). Assessment and prediction of air temperature in the DSF cavity is the most important simulation parameter and defining future operational model and control strategy are later mostly dependant on it. The coefficient of determination (R2) indicates a high degree of agreement between the result of measurements and model simulations. In each case (zone/mode), over 90% of the results obtained by the simulation model coincide with the measured values. The best results were observed in this indicator during the transient regimen in zone III (97%) and the worst in zone II, winter regime (90%). Low values of statistical indicators MBE and RSME were also obtained in this case which indicates a high level of agreement of results therefore pointing to the small deviations obtained by simulation (S) and measured value (M). MBE indicator values range from -1.36 (I zone, winter regime) to -2.88 (zone III, transient regime). As for the value of the indicator RSME, values range from 2.04 (I zone, winter regime) to 3.02 (III zone, summer regime). The coefficient of variation is slightly higher and varies in the range from 8.82% (I zone, summer regime) to 17.68% (III zone, winter regime) which can be considered as a range within

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the limits of acceptability, a good selection and validation of model parameters applied in the research . In the case of cavity DSF air temperature indicator DMIN (S-M) in zone I is positive for all three time regimes (winter, spring, summer), but negative in II and III zone, while DMAX (SM) has a negative value for all three regimes all three zones. Minimum values are close to zero, while the maximum go up to -6.07 in the third zone, the winter regime. Unlike the previous cases, the calculated indicators in the case of open and closed facade, point to considerable differences in the values obtained from previously commented cases simulation (Table 7). In reality, the building facade is always open, however, as a result of accidental fire dampers closing, there was a possibility to analyse the scenario when the façade interspace is closed. The coefficient of determination R2, when façade is open, indicates a well-defined simulation model, since it takes the value of about 86% with very small differences in values between the observed zones. On the other hand, the value of this indicator when the facade is closed is low (65% to 69%), which is below the standards stipulated limit of 75%. MBE indicator values are uniform, converging to zero, provided that an open façade is characterised by positive value of this indicator (the simulation values are larger than the measured values for the building) and a closed facade is characterised by negative one. Standard deviation is extremely low and very close to the ideal value, which together with MBE indicates an extremely well match of the actual measurement results and the results obtained by simulation and indicate in the well-defined model despite the low values of the coefficient of determination and distinctly. Even at first glance, unacceptable high values of the coefficient of variation which for open facade are in the interval ranging from 22.66% to 29.79%, and for closed in the range of 74.28% to as much as 87.57%. Such, at first glance paradoxical results do not find their footing in the bad simulation model defining, but in a small scale measurement

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range and the low value of the arithmetic mean which in closed façade varies between 0.04 to 0.06. Indicator minimum difference DMIN (S-M) indicates the positive differences present in an open system in zone II. In all other cases the differences are negative. Regarding the indicator of maximum difference DMAX (S-M), zones II and III stand out in the open system where the differences are positive, while in all other cases are negative. Small differences between simulation and measured values in the closed façade scenario, which DMAX (S-M) indicator value directs to, is another confirmation that a low coefficient of determination with the closed facade is not the result of ill-defined model. Moreover, big variability of the velocity of air in the DSF cavity is what should be emphasized and that the scope of its measurement must be reduced to the level below the minutes. The measurements were not able to be performed in shorter intervals because of the long seasonal period measurement intervals. During this analysis, time step of 15 minutes is long and does not give sufficient precision due to the rapid changes in the analysed parameters. The general conclusion which can be obtained from the statistical indicators is that the defined model is confirmed.

5. CONCLUSION Conducted experimental research primarily indicates key advantages and disadvantages in the application of the DSF concept. Further, it is necessary to draw and position a virtual model of the building with the selected DSF as a continuation of the analysis. Ultimate goal for creating a virtual and confirmed model was to further the research of energy simulation studies into the effects of DSF in various scenarios, which were not physically possible as the part of the experimental research. The combination of software tools EnergyPlus and proved to be a good and reasonable choice

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when it came to the relation of simulation accuracy and the time required for the simulation. Simulations typically lasted on average 10 minutes, which can be considered a relatively short time to obtain results. Software tools assume that the air in the zone is of the uniform temperature therefore giving only one simulated temperature value. Physical measurements obtained two air temperature values across the width of the facade, thus the average of the two measured values was used in the analysis. Same was the case for the temperatures of the observed surfaces, differing in the fact that one temperature value was measured for each physical surface. When the cavity air velocity of was analysed, there was a higher level of disagreement between measurement and simulation. The main obstacle is the limitation of the software package which provides results on the hourly basis, whereas the nature of the observed parameter changes its intensity every few seconds. In future research, the measurement interval must be lowered to the level of a few seconds. The output values of network flow algorithm Airflow Network generate volumetric air flow values (m3/s) which are divided by the cross-sectional area of the facade and with a value of "discharge" coefficient for the given virtual horizontal opening. In doing so, the resulting measured values and simulations become comparable. Looking at the two cases, opened and closed DSF, the results show that various factors affect air speed values. In the case where the facade is open, the value of the cavity air velocity markedly depends on the wind speed. Also, the day regime speed is also affected by the value of solar radiation, which implies that the value of the speed of air in the DSF cavity is directly proportional to the values of wind speed and intensity of solar radiation. Influence of the wind direction should be stated in addition. In the period of accidental fire dampers closing, there was a possibility to analyse the case of closed facade. It is obvious that the value of the speed depends only on the daily level of solar radiation, when the highest values of speed were recorded. All of the used statistical indicators have shown a high level of accuracy and matching between

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the results obtained by the simulation and measurement. Values differ depending on the observed zone and regime. Conclusion is that better degree of result matching was obtained in the interim and summer mode, especially when analysing the most dominant parameter - air temperature in the DSF. Further, the conclusion is that the larger deviations occur during the day, which may be reasoned as a complex thermal DSF behaviour which is translated to the imprecision and uncertainty of the convective heat transfer coefficient. When it comes to the value of air velocity, the conclusion is that this parameter must be analysed separately in a much shorter time intervals (interval of a few seconds). The obtained results related to air velocity indicate that the values coincide better when the flaps are down, i.e. when the observed case is better controlled. As an example, Figs 5-7, shows the two most representative statistical indicators, R2 and CV (RSME), in case of the DSF cavity air temperature during the whole period. The general conclusion for of the DSF model verification is that the simulation results represent a good forecast of the real (measured) values. Therefore, such a finely tuned model is highly reliable in terms of future evaluation of thermal performance of the building with DSF. It should also be noted that despite the lack of standards or guidelines for verification of temperature and air speed, objectively speaking, the reliability of the model is good in terms of the obtained values for the proposed statistical indicators. ACKNOWLEDGEMENTS This work is funded by ASHRAE (American Society of Heating, Refrigerating and AirConditioning Engineers). Aleksandar Anđelković would like to give appreciation to the ASHRAE Grant-in-Aid Fund and to the Life Member Club. Financial contribution for this research is supported from the club.

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[10] A. S. Anđelković, et all, Experimental research of the thermal characteristics of a multistorey naturally ventilated double skin façade, Energy and Buildings, 86, (2015) 766-781 [11] DOE, EnergyPlus 8.2 Engineering Reference: The Encyclopedic Reference to EnergyPlus Calculations, U.S. Department of Energy, 2014

20

[12] DOE, EnergyPlus 8.2 EnergyPlus input/output references, U.S. Department of Energy, 2014 [13] G.N. Walton, AIRNET – A Computer Program for Building Airflow Network Modeling, NISTIR 89-4072, National Institute of Standards and Technology, Gaithersburg, MD, 1989 [14] ASHRAE, ASHRAE Guideline 14-Measurement of Energy and Demand Savings, American Society of Heating, Refrigerating and Air-Conditioning Engineers, 2002 [15] U.S. DOE Federal Energy Management Program, Measurement and Verification Guidelines: Measurement and Verification for Federal Energy Projects Version 3.0, U.S. Department of Energy, 2008 [16] International Performance Measurement and Verification Protocol: Concepts and Options for Determining Energy and Water Savings, Vol I, Efficiency Valuation Organization, 2012 [17] H. Manz, at all, Series of experiments for empirical validation of solar gain modeling in building energy simulation codes-experimental setup, test cell characterization, specifications and uncertainty analysis, Building and Environment 41 (2006) 1784-1797 [18] A.L.S. Chan, T.T. Chow, K.F. Fong, Z. Lin, Investigation on energy performance of double skin facade in Hong Kong, Energy and Buildings 41 (2009) 1135-1142 [19] O. Kalyanova, et all, An empirical validation of building simulation software for modeling of doubleskin facade (DSF), Proceedings of 11th International Conference on Building Simulation, Glasgow, Scotland, 2009 [20] C. Marinosci, et all, Empirical validation and modelling of a naturally ventilated rainscreen facade building, Energy and Buildings 43 (2011) 853-886 [21] Z. Zhai, et all, Assessment of natural and hybrid ventilation models in whole-building energy simulations, Energy and Buildings 43 (2011) 2251-2261 [22] Y. Kim, et all, Contribution of natural ventilation in a double skin envelope to heating load reduction in winter, Building and Environment 44 (2009) 2236-2244 [23] N. Hashemi, et all, Thermal behaviour of a ventilated double skin facade in hot arid climate, Energy and Buildings 42 (2010) 1823-1832 [24] A. De Gracia, et all, Numerical modelling of ventilated facades: A review, Renewable and Sustainable Energy Reviews 22 (2013) 539-549

21

Fig 1: Research algorithm

22

Fig 2: Layout of the complex and selected part of the building envelope which is used for EnergyPlus model

Fig 3: Simulation model layout created in the Design Builder

23

Cavity air temperature - simulation, °C 50 45 40 35 30 25 20 15 10 5 0

Winter Season

Cavity air temperature - measurement, °C

Transitional season

Summer season

T h i r d z o n e

50

S e c o n d

45 40 35 30 25 20

z o n e

15 10 5 0 50 45

F i r s t

40 35 30 25

z o n e

20 15 10 5 0 50 45 40 35 30 25 20 15 10 5 0 -5 -10

3000 2500 2000 1500 1000 500 0 00 12 00 12 00 12 00 12 00 12 00 12 00 12 00 12 00 12 00 12 16/2 17/2 18/2 19/2 20/2 20/4 21/4 22/4 23/4 24/4 Outside air temperature, °C

00 12 5/7

00 12 6/7

00 12 7/7

00 12 8/7

00 12 00 9/7 10/7

Mean global solar radiation, W/m2

Fig 4: Validation results for the cavity air temperatures

24

Cavity air temperature - measurement, °C

40

FIRST ZONE 35

R² = 0.9738 CV(RSME)= 11,05%

30 25 20 15 10 5 5

10

15 20 25 Cavity air temperature - simulation, °C

30

35

Fig 5: Validation results for the cavity air temperatures

Cavity air temperature - measurement, °C

40

SECOND ZONE 35

R² = 0.9714 CV(RSME)= 11,97%

30 25 20 15 10 5

5

10

15 20 25 30 Cavity air temperature - simulation, °C

35

40

Fig 6: Validation results for the cavity air temperatures

25

Cavity air temperature - measurement, °C

40

THIRD ZONE 35

R² = 0.9724 CV(RSME)= 13,85%

30 25 20 15 10 5

5

10

15 20 25 30 Cavity air temperature - simulation, °C

35

40

Fig 7: Validation results for the cavity air temperatures

26

Table 1: Literature overview about validation results on models with DSF Structure type Lab model(4) Lab model (17) Lab model (18) Lab model (19) Lab model (20) Lab model (9) Real building (9) Real building (21) Real building (22) Real building (6) Real building (7) Real building (23)

Simulation software EnergyPlus EnergyPlus EnergyPlus ESP-r ESP-r EnergyPlus EnergyPlus EnergyPlus TRNSYS EnergyPlus EnergyPlus EnergyPlus

Average 2.91; 0.99 0.18; ― 0.27; ― 0.85; ― 0.45; ― 1.40; ― 0.37; ― 2.15; ― 1.87; ― 1.86; ― 2.25; 0.21 4.56; ―

Error (˚C; m/s) Typical maximum 20.06; 1.16 0.72; ― 4.91; ― 5.22; ― 15.31; ― 2.50; ― 1.4; ― 4.85; ― 4.17; ― 9.58; ― 8.74; 0.38 7.12; ―

R2 (%) ― ― ― ― 90.89 92.32 ― ― 96.58 94.62 90.31 ―

Table 2: Details about used EnergyPlus and AIRNET simulation parameters Simulation setings Solar distribution Surface convection algorithm (inside) Surface convection algorithm (outside) Time step per hour Airflow model Cp imput values Discharge coefficient of the openings between the floors Crack Flow trough windows and walls Crack Flow trough vents Air mass flow exponent People Lights Electric equipment Zone Equipment Room temperature – winter season Room temperature – summer season

Selected type Full interior and exterior AdaptiveConvectionAlgorithm MoWiTT 12 AIRNET Network Algorithm Gp generator 0.51 Air mass coefficient (0.00015/0.00025) 0.0015 0.68/0.68; vents 0.64 People/Area 0.15 Watts/Area 12 Watts/Area 10 ZoneHVAC:IdealLoadsAirSystem Between 6AM and 7PM, 23°C Other 17°C Between 6AM and 7PM, 23.5°C Other 28°C

27

Table 3: Validation results for the inside surface temperatures of the outer DSF layer Outer glass

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

I

0.96

0.93

0.94

0.94

-0.50

0.36

-0.64

-0.85

1.87

1.94

1.62

2.02

9.16

14.25

8.60

7.01

0.01

0.01

0.02

-0.03

6.93

6.93

-5.17

-6.70

II

0.96

0.93

0.94

0.94

-0.71

0.16

-0.84

-1.01

1.94

1.92

1.72

2.16

9.35

13.69

8.99

7.40

0.00

0.00

0.01

-0.01

-6.90

6.73

-5.37

-6.90

III

0.96

0.93

0.96

0.94

-1.02

-0.15

-1.15

-1.25

2.09

1.93

1.90

2.39

9.86

13.38

9.71

8.10

0.00

0.00

0.00

-0.01

-7.20

6.43

-5.67

-7.20

R2

MBE

RSME

CVRSME

Dmin (S-M)

Dmax (S-M)

Table 4: Validation results for the window surface temperatures of the inner DSF layer Window

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

I

0.95

0.91

0.93

0.92

-0.49

0.38

-0.63

-0.85

1.85

1.93

1.60

1.98

8.05

11.48

7.45

6.49

0.01

0.01

0.02

-0.03

6.93

6.93

-5.17

-5.84

II

0.95

0.92

0.93

0.93

-0.98

-0.11

-1.12

-1.25

2.02

1.90

1.83

2.31

8.42

10.58

8.10

7.31

0.00

0.00

0.00

-0.01

6.43

6.43

-5.67

-5.81

III

0.96

0.92

0.96

0.93

-1.48

-0.62

-1.62

-1.66

2.30

1.99

2.17

2.69

9.30

10.64

9.29

8.33

-0.02

-0.02

0.05

-0.02

-6.18

5.93

-6.17

-6.18

R2

MBE

RSME

CVRSME

Dmin (S-M)

Dmax (S-M)

Table 5: Validation results for the wall surface temperatures of the inner DSF layer Wall

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

I

0.93

0.92

0.83

0.86

0.60

1.98

0.20

-0.48

2.14

2.68

1.86

1.77

10.67

19.54

9.86

6.36

0.00

0.04

0.00

0.01

6.54

5.63

6.54

-4.92

II

0.93

0.92

0.86

0.87

0.55

1.98

0.08

-0.51

2.13

2.68

1.80

1.79

10.32

18.70

9.34

6.29

0.00

0.07

0.00

0.02

4.88

4.88

-4.78

-4.57

III

0.93

0.93

0.93

0.88

0.55

1.98

0.08

-0.51

2.13

2.68

1.80

1.79

10.03

17.93

9.05

6.18

0.00

0.00

0.01

0.00

-5.66

5.24

-5.66

-4.75

R2

MBE

RSME

CVRSME

Dmin (S-M)

Dmax (S-M)

Table 6: Validation results for the cavity air temperatures Cavity air

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

Σ

W

T

S

I

0.97

0.90

0.94

0.95

-1.86

-1.36

-1.93

-1.73

2.22

2.04

2.11

2.47

11.05

15.04

11.31

8.82

0.01

0.01

0.08

0.06

-5.07

-5.07

-4.32

-5.00

II

0.97

0.90

0.95

0.95

-2.15

-1.57

-2.28

-1.94

2.49

2.27

2.43

2.74

11.97

15.88

12.52

9.53

-0.02

-0.02

-0.16

-0.12

-5.47

-5.47

-4.72

-5.40

III

0.97

0.90

0.98

0.96

-2.75

-2.17

-2.88

-2.43

3.02

2.72

3.00

3.31

13.85

17.68

14.71

11.13

0.00

0.00

-0.76

-0.38

-6.07

-6.07

-5.32

-6.00

R2

MBE

RSME

CVRSME

Dmin (S-M)

Dmax (S-M)

28

Table 7: Validation results for cavity air velocity Cavity air velocity

I

II

III

I

II

III

I

II

III

I

II

III

I

II

III

I

II

III

DSF Opened

0.86

0.86

0.86

0.03

0.05

0.06

0.10

0.10

0.12

22.66

25.66

29.79

-0.01

0.01

0.00

-0.18

0.18

0.20

DSF Closed

0.69

0.65

0.70

-0.03

-0.04

-0.04

0.03

0.05

0.05

74.28

87.57

86.38

-0.01

-0.06

-0.08

-0.05

-0.06

-0.07

R2

MBE

RSME

CVRSME

Dmin (S-M)

Dmax (S-M)

29