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Assessing Lighting Energy Saving Potential from Daylight Harvesting in Office Buildings Based on Code Compliance & Simulation Techniques: A Comparison
Available online at www.sciencedirect.com
ScienceDirect Procedia Environmental Sciences 38 (2017) 420 – 427
International Conference on Sustainable Synergies from Buildings to the Urban Scale, SBE16
Assessing Lighting Energy Saving Potential from Daylight Harvesting in Office Buildings Based on Code Compliance & Simulation Techniques: A Comparison A. Tsangrassoulisa,*, A. Kontadakisa, L. Doulosb a University of Thessaly/Department of Architecture, , Pedion Areos, 383 34 Volos, Greece Lighting Lab, National Technical University of Athens, Heroon Politechniou 9, 157 80 Zografou, Greece
b
Abstract Daylighting is a cornerstone strategy aiming at reducing a building’s energy consumption. Daylight responsive dimming is a proven, mature technology, that significantly reduces lighting energy use, which in turn, affects both energy consumption and peak loads as well. Although in Energy Performance Building Directive (EPBD), the use of a daylighting harvesting system is encouraged, the specifications are rather limited based on EN 15193:2007. According to literature, the potential for lighting energy savings from daylight integration, can approach 60.0%. Consequently, a more accurate and detailed calculation method could possible result in downgrading a building’s energy performance rating, when compared with simplified procedures that may underestimate these savings. The paper builds on the authors’ previous work, providing an overview of the methods and techniques used to estimate lighting energy savings due to daylight harvesting. An extensive comparison is carried out between the resulting energy savings as estimated by the method adopted in EN 15193:2007 and its variation by the Greek Regulation for the Energy Efficiency of Buildings (KENAK), with savings calculated by more complex calculation methods that require the use of simulation tools. According to the results, great differences are observed in the estimated lighting energy savings between simulation and EN15193:2007. These differences still exist even when the parameters used in EN15193 are calculated by an external software affecting energy performance certificate rating. © 2017 2017The TheAuthors. Authors. Published by Elsevier © Published by Elsevier B.V. B.V. This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the organizing committee of SBE16. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of SBE16. Keywords: Lighting controls, Dimming, Energy efficiency, Daylight, EN 15193:2007 simplified method, Simulation techniques
*
Corresponding author. E-mail address: [email protected]
1878-0296 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of SBE16. doi:10.1016/j.proenv.2017.03.127
A. Tsangrassoulis et al. / Procedia Environmental Sciences 38 (2017) 420 – 427
1. Intoduction The reduction of lighting consumption plays a key role in achieving EU climate and energy objectives, to be met by 2020 as lighting represents one of the quickest return on up-front investments [1]. Within EU-27 tertiary sector, lighting represents the largest percentage (~21%) of electricity consumption [2]. Furthermore, lighting make a significant contribution to the formation of peak electricity demand since it coincides with various other electrical loads. In Greece, the primary energy factor for electricity generation is 2.9 according to the technical guidelines [3] accompanying the implementation of Energy Performance Building Directive. This makes lighting have a decisive influence on primary energy consumption and since the building’s Energy Performance Certificate is based on the relative difference between building’s primary energy consumption and the primary consumption of a notional building (asset rating), it can affect the building’s energy performance certificate rating. Reducing lighting consumption can be achieved either by reducing the installed power (more efficient equipment/design) or by reducing operation time through (among other techniques) daylight exploitation. Although a small percentage (2% in USA) of commercial building have daylight sensors [4], this type of control can reduce lighting energy consumption by up to 60% [5] depending on the use of space. Using energy audits from 2011-2014 Lepida [6] it was estimated that for office buildings in Greece the calculated primary consumption is 100.7 kwh/m2 for heating, 86.3 kWh/m2 for cooling and 139 kWh/m2 for lighting. Thus any reduction in lighting energy consumption by 40% (modest estimate) will reduce the overall primary energy by 17% which is more than 50% of the reduction needed for an improvement in EPC rating without changing any other building system. It is therefore necessary to make use of a reliable daylight calculation methodology capable of combining reduced calculation time, ease of use and accuracy. Significantly different annual lighting energy consumption observed even among EN15193:2007 simplified and comprehensive approach [7]. This paper compares results regarding lighting energy savings due to daylight as these are calculated using EN15193:2007 [8] and detailed simulations with DAYSIM 3.1e [9]. A typical office space was used, with three different Glazing to Wall Ratios (10%, 20%, 100%), two orientations (south, north) and three different shading systems . The estimation of lighting energy savings was based : a) on EN15193 methods both simplified and comprehensive b) on daylight autonomy (the percentage of operating time with daylight illuminance above a predefined level i.e. 500 lux) c) on a complete modelling of a dimming system and finally on the TEE-KENAK software that was developed for the necessary national EPBD implementation. 2. Methodology The room that was used for the simulations is a typical space in an office building and its internal dimensions oare3.4 x 5.4 x 2.7 m with one external opening. Similar spaces have been used in the past for various research projects [5]. The electric lighting system consisted of two ceiling mounted fluorescent lamp (T26 2x58W) luminaires in a uniform layout with dimmable ballasts. The installed power was 11.9 W/m2 while the average maintenance lighting levels on the working surface (0.8 m height) were 592 lux with 0.6 uniformity (minimum to average value). Both luminaires are inside the perimeter zone as this is defined by EN 15193:2007, thus, it can be controlled with one sensor. Wall, ceiling and floor reflectances are 0.5, 0.8 and 0.2 respectively while glazing transmittance is 0.65. Three façade geometries were tested with 10%, 20% (sill height 1 m) and 100% Glazing to Wall Ratios (GWR). The types of shading systems that were examined are: Two external obstructions at a 10m distance from the space’s façade with a height equal to 10m (Fig. 1a) and 17 m (Fig. 1b) respectively. In both cases, the obstruction length is 200 m. An overhang with a 1m length for cases of GWR 10% (Fig. 1c) and 20% (Fig. 1c) and 1.5m length for the case of 100% GWR (Fig. 1d).
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Fig. 1. Cases {a} and {b} were used for both south and north orientations. Cases {c} and {d} only for south orientation with {c} used for 10% and 20% GWR and {d} for 100% GWR. The surface reflectance of exterior obstructions is similar to that of the ground, 0.2.
In total twenty-one cases were examined calculating lighting energy savings using as presented in the following table. Table 1. Test cases examined No of test cases #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12 #13 #14 #15 #16 #17 #18 #19 #20 #21
Orientation North North North North North North North North North South South South South South South South South South South South South
Glazing to Wall Ratio 10% 20% 100% 10% 20% 100% 10% 20% 100% 10% 20% 100% 10% 20% 100% 10% 20% 100% 10% 20% 100%
Shading No No No Figure 1 (a) Figure 1 (a) Figure 1 (a) Figure 1 (b) Figure 1 (b) Figure 1 (b) No No No Figure 1 (c) Figure 1 ( c ) Figure 1 (d) Figure 1 (a) Figure 1 (a) Figure 1 (a) Figure 1 (b) Figure 1 (b) Figure 1 (b)
The comparison of the lighting energy savings as these are calculated using different methodologies, require similar initial assumptions, which in our case are as follows. There is no parasitic energy consumption required to charge emergency lighting luminaires and for standby energy for lighting controls. The operating hours are 2530 during the daylight time and 80 during the non-daylight time (8:00-18:00 not counting weekends). According to EN 15193:2007 for the examined cases, the annual energy consumption used for illumination is estimated using the formula (without taking parasitic energy into account): WL,t = (Pn * Fc) *[(tD * Fo * FD) + (tN * Fo)]/1000 in kWh
(1)
Where Pn is the installed power, tD and tN are daylight and non-daylight time usage, while Fc, Fo and FD are factors relating to the constant illuminance, occupancy and daylight linked controls respectively. Estimation of F D when an annual energy consumption is needed is done with the following formula: FD=1-FDSFDC
(2)
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Where FDS is the daylight supply factor representing the contribution of daylight to the illuminance required and FDC is the daylight control factor depending on the selected system. In our case Fc=1, Fo=1 and thus, the equation (1) becomes: WL,t = (Pn) *[(tD * FD) + (tN)]/1000 in kWh
(3)
The role of the daylight supply factor calculation is important. It can be estimated using the average daylight factor (DF) in the daylight zone which can be calculated either with an external software or according to the simplified methodology proposed in EN15193:2007. Different approaches result in different FDS values. In an effort to overcome the problem, the impact of the fenestration and the shading system on daylight penetration is categorized into bin classes of strong, medium, weak and no access of the zone to daylight. Therefore, although the average DF value can vary continuously, FDS and FDC can have only four possible values depending on the requested maintained illuminance and daylight factor value in the daylight zone. Therefore, if different methods are used to calculate the DF and the values estimated are on opposite sides of the borderline between the bin classes of daylight penetration, quite large differences will be observed in the annual lighting consumption. Substituting the known parameters (Pn=220 Watt, tD=2530, tN=80), equation (3) becomes: WL,t = 574.2-556.6* FDSFDC
(5 )
The relative differences in annual lighting energy consumption due to class differentiation are presented in the following table: Table 2. Relative difference in annual lighting energy consumption among the four daylight penetration class. Maintained illuminance 300 lux Relative difference 500 lux Relative difference 750 lux Relative difference
No (DF<1%) 574.2 kWh 574.2 kWh 574.2 kWh
Weak (2%>DF>=1%) 240.4 kWh -58.1% 328.0 kWh -42.8% 403.1 kWh -29.7%
Medium (3%>DF >=2%) 184.4 kWh -23.2% 231.5 kWh -29.4% 304.3 kWh -24.5%
Strong (DF>=3%) 120.3 kWh -34.7% 148.7 kWh -35.7% 186.5 kWh -38.7%
Examining the results of the table, it seems that if a small differentiation of DF leads to a different class, the induced differences in annual lighting energy consumption can vary from -23.2% to -58.1%. These differences are quite large justifying a discussion on the method used. For the examined twenty-one cases a comparison was made between DF values obtained using the simplified method presented in EN 15193:2007 an external software (Dialux 4.12, [9]) using default settings (light losses equal to 1) and DAYSIM 3.1e with the following parameters (-ab:5, -ad 1500, -as:500, -ar:300). Dialux 4.12, widely used by lighting design practices, was recently compared [11] against the analytical test cases of CIE 171:2006 [12] concluding that, in general, the software has low accuracy in calculating sky and external components in daylight scenes. DAYSIM is a validated daylight analysis software.
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Fig. 2. Average daylight factors for the daylight zone as calculated using EN 15173:2007 method, Dialux 4.12 and DAYSIM 3.1e software. Daylight penetration classes are presented as well together.
From the data shown in Fig. 2, it is evident that in the majority of the cases, changes in the calculation methodology, can change the class category affecting both FDS and FDC and finally the calculation of energy consumption. Only 19% of the cases examined belong to the same class with all methods used and this happens when 100% GWR is used and thus large values of DF are achieved. DFs calculated with the simplified method of EN 15193 are the lowest for all cases and quite often (57% of all cases) correspond to the lowest daylight penetration class (no access) systematically downgrading lighting energy savings. Avoiding the calculation of DFs in the daylight zone, lighting energy consumption can be estimated directly using equation (5) and daylight simulation to calculate FDS according to the following equation [13] . ಲ
ܨௌ ൌ
బ ாವ ௗ௧ ாೞ כ௧ಲ
(6)
where E is the illuminance due to daylight, E is the design illuminance and t operational time. In this equation D S A whenever E
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Fig. 3. Comparison of FDS values as these calculated using the default methodology of EN15193:2007 and simulation. No correction was applied due to longer operational time.
Based on the above mentioned results, using simulation to calculate FDS results in reduced lighting energy consumption. Relative differences are negative for 20 out of 21 cases with range varing between 6.3% to -40.7%. In summary, the previous analysis has shown that quite large differences in lighting energy consumption may occur depending on the method used to estimate the parameters needed to apply EN15193:2007 method. However there are some additional methods as to estimate lighting energy savings due to daylighting. Again DAYSIM 3.1e was used to provide the hourly illuminance values on the working surface (198 grid points) for the typical metrological year for Athens, Greece. Three approaches were selected . The first one is the calculation of Daylight Autonomy (DA) which is the percentage of the operational hours that a given point in a room is above a specified illuminance value. Οnly points lying within the perimeter daylighting zone were used , in order for the results to be compatible with these achieved using the methodology of EN15193:2007. The second method used is the simulation of an ideal dimming system using the values of horizontal daylight illuminances. There is a linear relationship between fractional input power fP and fractional lighting output fL. The latter is calculated according to the relationship: fL=max[0, (500-Daylight levels at a point)/500]
(7)
When the minimum lighting output is achieved (fLmin), there is a minimum power input fPmin. Both values depend on the type of ballast. The relation between power fP and fL is : If fL
(8)
For this study fPmin equals 0.15 while fLmin 0.01. The third method is similar to the second one but the control is performed according to the illuminance on a sensor with 700 field of view placed in the middle of the space facing downwards. The ballast characteristics used are the same as above. Although in the Greek TEE-KENAK software, the percentage of daylight zone in the space is mentioned, daylighting is ignored. Thus the window size or shading system have no role and possible enegy savings calculated based on two parameters. The efficiency of a control system in exploring the given saving potential (manual or automatic) and the function of the lighting control system (Manual On / Off Switch, Auto On / Dimmed etc). Nevertheless, the maximum energy savings achieved are 10.8%. Lighting energy savings percentage as calculated for all cases and all methodologies are presented in the following graph.
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Fig. 4. Estimation of possible lighting energy savings using five different approaches to calculate the impact of daylight.
Lighting energy savings calculated using climate based daylight modelling are potentially more accurate that the daylight factor assessment and combines the quality of the light (i.e. potentially glary sunlight) with qualitative parameters, the shortcoming of this method is a significantly more complicated method of evaluation. Thus, using DA and ideal dimming results in greater energy savings in comparison to EN 15193 with externally calculated DF. An ideal dimming methodology shows an increase in calculated energy savings from 5.3% to 79.5% in comparison to those obtained from EN15193. Daylight Aytonomy can be correlated to energy savings as well but still its differences with EN15193 range between -27.1% to 35.6%. It seems that EN15193 methodology is more conservative in estimating lighting energy savings than climate based daylight simulation. Similar results were obtained in another study with external obstructions [14]. The latter has to take into account the control strategy used for the activation of the shading system. 3. Conclusions There is great interest by commercial building owners in the potential impact of energy performance certificate (EPC) rating on property values and rents without the results so far support a strong correlation. Kok and Jennen [15] presented an analysis of some 1100 recent rental transactions in the Netherlands and it seems a less energy efficient building archives a 6.5% lower rent in comparison to a more efficient one. Nevertheless, although a longer time period of analysis is needed to correlate EPC ratings and revenues, any improvement on the methodology used for these ratings is welcome. In Greece , according to data from recent energy audits [16] , lighting represents the largest share of primary energy between heating and cooling and daylight exploitation is an essential strategy for increasing energy savings in office buildings. Thus the methodology used to estimate these savings will affect EPC rating and need to be accurate. From the case studies examined in this paper it seems that the national TEE-KENAK approach is totally inappropriate since lighting savings are set to a minimum value considerably downgrading building energy rating. Hourly simulations are feasible today since new daylight metrics are already used based on dynamic climatic modelling. Therefore in the effort to increase the reliability of EPCs, the adoption of a more accurate methodology for the estimation of savings due to daylight harvesting is essential. In our opinion a consensus has to be reached on the exact procedures needed to estimate EN15193:2007 parameters.
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4. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.
McKinsey & Company, “Unlocking Energy Efficiency in the U.S. economy”, McKinsey Global Energy and Materials Report,2009. Paolo Bertoldi,Bettina Hirl,Nicola Labanca, Energy Efficiency Status Report, European Commission Joint Research Centre Institute for Energy and Transport, 2012, ISSN 1831-9424 TOTEE 20701-1/2010, Technical Guideline “National Standards for the estimation of parameters used to calculate energy efficiency of buildings and used to issue the energy performance certificates”, 2010. Alison Williams, Barbara Atkinson, Karina Garbesi, and Francis Rubinstein, Erik Page “A Meta-Analysis of Energy Savings from Lighting Controls in Commercial Buildings”, Nov.2011, LBNL 5095E L. Doulos, A. Tsangrassoulis, F. Topalis,, “Quantifying energy savings in daylight responsive systems: The role of dimming electronic ballasts”, Energy and Buildings, Energy and Buildings, 2008, Vol. 40, , 36-50 P. Lepida, “The impact of daylight harvesting to the energy balance of office buildings”, Hellenic Open University , Msc thesis , Sep. 2014 (in greek) Michele Zinzi, Alessandro Mangione, “The daylighting contribution in the electric lighting energy uses: EN standard and alternative method comparison”, Energy Procedia 78 , 2015 , 2663 – 2668. EN15193:2007, “Energy performance of buildings — Energy requirements for lighting” DAYSIM 3.1e, http://daysim.ning.com/ DIALux, https://www.dial.de/en/dialux/download/ Rizki A. Mangkuto, “Validation of DIALux 4.12 and DIALux evo 4.1 against the Analytical Test Cases of CIE 171:2006”, LEUKOS, 2016,12:139–150. CIE 171:2006, "Test Cases for Assessment of Accuracy of Lighting Computer Programs", International Commission of Illumination, ISBN 978 3 901906 47 3 De Boer, J.; Erhorn, H., Ein einfaches Modell zur Klassifizierung der Tageslichtversorgung von Innenräumen mit vertikalen Fassaden. gi Gesundheits-ingenieur 125 ,2004, H. 6, .281-295 A. Tsangrassoulis, A.Roetzel, L. Doulos, F. Topalis, “Comparison of lighting energy savings methodologies due to daylight with EN 15193”, 3rd Int. Conference on Passive and Low Energy Cooling for the Built Environment, 29/9-1/10 2010, Rhodes, Greece. Nils Kok, Maarten Jennen, “The impact of energy labels and accessibility on office rents”, Energy Policy, Volume 46, July 2012, Pages 489-497 Greenpeace Greece, Report “Transforming current situation in the building sector using the sun as ally”, Nov. 2015 (in greek)
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ScienceDirect Procedia Environmental Sciences 38 (2017) 420 – 427
International Conference on Sustainable Synergies from Buildings to the Urban Scale, SBE16
Assessing Lighting Energy Saving Potential from Daylight Harvesting in Office Buildings Based on Code Compliance & Simulation Techniques: A Comparison A. Tsangrassoulisa,*, A. Kontadakisa, L. Doulosb a University of Thessaly/Department of Architecture, , Pedion Areos, 383 34 Volos, Greece Lighting Lab, National Technical University of Athens, Heroon Politechniou 9, 157 80 Zografou, Greece
b
Abstract Daylighting is a cornerstone strategy aiming at reducing a building’s energy consumption. Daylight responsive dimming is a proven, mature technology, that significantly reduces lighting energy use, which in turn, affects both energy consumption and peak loads as well. Although in Energy Performance Building Directive (EPBD), the use of a daylighting harvesting system is encouraged, the specifications are rather limited based on EN 15193:2007. According to literature, the potential for lighting energy savings from daylight integration, can approach 60.0%. Consequently, a more accurate and detailed calculation method could possible result in downgrading a building’s energy performance rating, when compared with simplified procedures that may underestimate these savings. The paper builds on the authors’ previous work, providing an overview of the methods and techniques used to estimate lighting energy savings due to daylight harvesting. An extensive comparison is carried out between the resulting energy savings as estimated by the method adopted in EN 15193:2007 and its variation by the Greek Regulation for the Energy Efficiency of Buildings (KENAK), with savings calculated by more complex calculation methods that require the use of simulation tools. According to the results, great differences are observed in the estimated lighting energy savings between simulation and EN15193:2007. These differences still exist even when the parameters used in EN15193 are calculated by an external software affecting energy performance certificate rating. © 2017 2017The TheAuthors. Authors. Published by Elsevier © Published by Elsevier B.V. B.V. This is an open access article under the CC BY-NC-ND license Peer-review under responsibility of the organizing committee of SBE16. (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of SBE16. Keywords: Lighting controls, Dimming, Energy efficiency, Daylight, EN 15193:2007 simplified method, Simulation techniques
*
Corresponding author. E-mail address: [email protected]
1878-0296 © 2017 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Peer-review under responsibility of the organizing committee of SBE16. doi:10.1016/j.proenv.2017.03.127
A. Tsangrassoulis et al. / Procedia Environmental Sciences 38 (2017) 420 – 427
1. Intoduction The reduction of lighting consumption plays a key role in achieving EU climate and energy objectives, to be met by 2020 as lighting represents one of the quickest return on up-front investments [1]. Within EU-27 tertiary sector, lighting represents the largest percentage (~21%) of electricity consumption [2]. Furthermore, lighting make a significant contribution to the formation of peak electricity demand since it coincides with various other electrical loads. In Greece, the primary energy factor for electricity generation is 2.9 according to the technical guidelines [3] accompanying the implementation of Energy Performance Building Directive. This makes lighting have a decisive influence on primary energy consumption and since the building’s Energy Performance Certificate is based on the relative difference between building’s primary energy consumption and the primary consumption of a notional building (asset rating), it can affect the building’s energy performance certificate rating. Reducing lighting consumption can be achieved either by reducing the installed power (more efficient equipment/design) or by reducing operation time through (among other techniques) daylight exploitation. Although a small percentage (2% in USA) of commercial building have daylight sensors [4], this type of control can reduce lighting energy consumption by up to 60% [5] depending on the use of space. Using energy audits from 2011-2014 Lepida [6] it was estimated that for office buildings in Greece the calculated primary consumption is 100.7 kwh/m2 for heating, 86.3 kWh/m2 for cooling and 139 kWh/m2 for lighting. Thus any reduction in lighting energy consumption by 40% (modest estimate) will reduce the overall primary energy by 17% which is more than 50% of the reduction needed for an improvement in EPC rating without changing any other building system. It is therefore necessary to make use of a reliable daylight calculation methodology capable of combining reduced calculation time, ease of use and accuracy. Significantly different annual lighting energy consumption observed even among EN15193:2007 simplified and comprehensive approach [7]. This paper compares results regarding lighting energy savings due to daylight as these are calculated using EN15193:2007 [8] and detailed simulations with DAYSIM 3.1e [9]. A typical office space was used, with three different Glazing to Wall Ratios (10%, 20%, 100%), two orientations (south, north) and three different shading systems . The estimation of lighting energy savings was based : a) on EN15193 methods both simplified and comprehensive b) on daylight autonomy (the percentage of operating time with daylight illuminance above a predefined level i.e. 500 lux) c) on a complete modelling of a dimming system and finally on the TEE-KENAK software that was developed for the necessary national EPBD implementation. 2. Methodology The room that was used for the simulations is a typical space in an office building and its internal dimensions oare3.4 x 5.4 x 2.7 m with one external opening. Similar spaces have been used in the past for various research projects [5]. The electric lighting system consisted of two ceiling mounted fluorescent lamp (T26 2x58W) luminaires in a uniform layout with dimmable ballasts. The installed power was 11.9 W/m2 while the average maintenance lighting levels on the working surface (0.8 m height) were 592 lux with 0.6 uniformity (minimum to average value). Both luminaires are inside the perimeter zone as this is defined by EN 15193:2007, thus, it can be controlled with one sensor. Wall, ceiling and floor reflectances are 0.5, 0.8 and 0.2 respectively while glazing transmittance is 0.65. Three façade geometries were tested with 10%, 20% (sill height 1 m) and 100% Glazing to Wall Ratios (GWR). The types of shading systems that were examined are: Two external obstructions at a 10m distance from the space’s façade with a height equal to 10m (Fig. 1a) and 17 m (Fig. 1b) respectively. In both cases, the obstruction length is 200 m. An overhang with a 1m length for cases of GWR 10% (Fig. 1c) and 20% (Fig. 1c) and 1.5m length for the case of 100% GWR (Fig. 1d).
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Fig. 1. Cases {a} and {b} were used for both south and north orientations. Cases {c} and {d} only for south orientation with {c} used for 10% and 20% GWR and {d} for 100% GWR. The surface reflectance of exterior obstructions is similar to that of the ground, 0.2.
In total twenty-one cases were examined calculating lighting energy savings using as presented in the following table. Table 1. Test cases examined No of test cases #1 #2 #3 #4 #5 #6 #7 #8 #9 #10 #11 #12 #13 #14 #15 #16 #17 #18 #19 #20 #21
Orientation North North North North North North North North North South South South South South South South South South South South South
Glazing to Wall Ratio 10% 20% 100% 10% 20% 100% 10% 20% 100% 10% 20% 100% 10% 20% 100% 10% 20% 100% 10% 20% 100%
Shading No No No Figure 1 (a) Figure 1 (a) Figure 1 (a) Figure 1 (b) Figure 1 (b) Figure 1 (b) No No No Figure 1 (c) Figure 1 ( c ) Figure 1 (d) Figure 1 (a) Figure 1 (a) Figure 1 (a) Figure 1 (b) Figure 1 (b) Figure 1 (b)
The comparison of the lighting energy savings as these are calculated using different methodologies, require similar initial assumptions, which in our case are as follows. There is no parasitic energy consumption required to charge emergency lighting luminaires and for standby energy for lighting controls. The operating hours are 2530 during the daylight time and 80 during the non-daylight time (8:00-18:00 not counting weekends). According to EN 15193:2007 for the examined cases, the annual energy consumption used for illumination is estimated using the formula (without taking parasitic energy into account): WL,t = (Pn * Fc) *[(tD * Fo * FD) + (tN * Fo)]/1000 in kWh
(1)
Where Pn is the installed power, tD and tN are daylight and non-daylight time usage, while Fc, Fo and FD are factors relating to the constant illuminance, occupancy and daylight linked controls respectively. Estimation of F D when an annual energy consumption is needed is done with the following formula: FD=1-FDSFDC
(2)
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Where FDS is the daylight supply factor representing the contribution of daylight to the illuminance required and FDC is the daylight control factor depending on the selected system. In our case Fc=1, Fo=1 and thus, the equation (1) becomes: WL,t = (Pn) *[(tD * FD) + (tN)]/1000 in kWh
(3)
The role of the daylight supply factor calculation is important. It can be estimated using the average daylight factor (DF) in the daylight zone which can be calculated either with an external software or according to the simplified methodology proposed in EN15193:2007. Different approaches result in different FDS values. In an effort to overcome the problem, the impact of the fenestration and the shading system on daylight penetration is categorized into bin classes of strong, medium, weak and no access of the zone to daylight. Therefore, although the average DF value can vary continuously, FDS and FDC can have only four possible values depending on the requested maintained illuminance and daylight factor value in the daylight zone. Therefore, if different methods are used to calculate the DF and the values estimated are on opposite sides of the borderline between the bin classes of daylight penetration, quite large differences will be observed in the annual lighting consumption. Substituting the known parameters (Pn=220 Watt, tD=2530, tN=80), equation (3) becomes: WL,t = 574.2-556.6* FDSFDC
(5 )
The relative differences in annual lighting energy consumption due to class differentiation are presented in the following table: Table 2. Relative difference in annual lighting energy consumption among the four daylight penetration class. Maintained illuminance 300 lux Relative difference 500 lux Relative difference 750 lux Relative difference
No (DF<1%) 574.2 kWh 574.2 kWh 574.2 kWh
Weak (2%>DF>=1%) 240.4 kWh -58.1% 328.0 kWh -42.8% 403.1 kWh -29.7%
Medium (3%>DF >=2%) 184.4 kWh -23.2% 231.5 kWh -29.4% 304.3 kWh -24.5%
Strong (DF>=3%) 120.3 kWh -34.7% 148.7 kWh -35.7% 186.5 kWh -38.7%
Examining the results of the table, it seems that if a small differentiation of DF leads to a different class, the induced differences in annual lighting energy consumption can vary from -23.2% to -58.1%. These differences are quite large justifying a discussion on the method used. For the examined twenty-one cases a comparison was made between DF values obtained using the simplified method presented in EN 15193:2007 an external software (Dialux 4.12, [9]) using default settings (light losses equal to 1) and DAYSIM 3.1e with the following parameters (-ab:5, -ad 1500, -as:500, -ar:300). Dialux 4.12, widely used by lighting design practices, was recently compared [11] against the analytical test cases of CIE 171:2006 [12] concluding that, in general, the software has low accuracy in calculating sky and external components in daylight scenes. DAYSIM is a validated daylight analysis software.
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Fig. 2. Average daylight factors for the daylight zone as calculated using EN 15173:2007 method, Dialux 4.12 and DAYSIM 3.1e software. Daylight penetration classes are presented as well together.
From the data shown in Fig. 2, it is evident that in the majority of the cases, changes in the calculation methodology, can change the class category affecting both FDS and FDC and finally the calculation of energy consumption. Only 19% of the cases examined belong to the same class with all methods used and this happens when 100% GWR is used and thus large values of DF are achieved. DFs calculated with the simplified method of EN 15193 are the lowest for all cases and quite often (57% of all cases) correspond to the lowest daylight penetration class (no access) systematically downgrading lighting energy savings. Avoiding the calculation of DFs in the daylight zone, lighting energy consumption can be estimated directly using equation (5) and daylight simulation to calculate FDS according to the following equation [13] . ಲ
ܨௌ ൌ
బ ாವ ௗ௧ ாೞ כ௧ಲ
(6)
where E is the illuminance due to daylight, E is the design illuminance and t operational time. In this equation D S A whenever E
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Fig. 3. Comparison of FDS values as these calculated using the default methodology of EN15193:2007 and simulation. No correction was applied due to longer operational time.
Based on the above mentioned results, using simulation to calculate FDS results in reduced lighting energy consumption. Relative differences are negative for 20 out of 21 cases with range varing between 6.3% to -40.7%. In summary, the previous analysis has shown that quite large differences in lighting energy consumption may occur depending on the method used to estimate the parameters needed to apply EN15193:2007 method. However there are some additional methods as to estimate lighting energy savings due to daylighting. Again DAYSIM 3.1e was used to provide the hourly illuminance values on the working surface (198 grid points) for the typical metrological year for Athens, Greece. Three approaches were selected . The first one is the calculation of Daylight Autonomy (DA) which is the percentage of the operational hours that a given point in a room is above a specified illuminance value. Οnly points lying within the perimeter daylighting zone were used , in order for the results to be compatible with these achieved using the methodology of EN15193:2007. The second method used is the simulation of an ideal dimming system using the values of horizontal daylight illuminances. There is a linear relationship between fractional input power fP and fractional lighting output fL. The latter is calculated according to the relationship: fL=max[0, (500-Daylight levels at a point)/500]
(7)
When the minimum lighting output is achieved (fLmin), there is a minimum power input fPmin. Both values depend on the type of ballast. The relation between power fP and fL is : If fL
(8)
For this study fPmin equals 0.15 while fLmin 0.01. The third method is similar to the second one but the control is performed according to the illuminance on a sensor with 700 field of view placed in the middle of the space facing downwards. The ballast characteristics used are the same as above. Although in the Greek TEE-KENAK software, the percentage of daylight zone in the space is mentioned, daylighting is ignored. Thus the window size or shading system have no role and possible enegy savings calculated based on two parameters. The efficiency of a control system in exploring the given saving potential (manual or automatic) and the function of the lighting control system (Manual On / Off Switch, Auto On / Dimmed etc). Nevertheless, the maximum energy savings achieved are 10.8%. Lighting energy savings percentage as calculated for all cases and all methodologies are presented in the following graph.
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Fig. 4. Estimation of possible lighting energy savings using five different approaches to calculate the impact of daylight.
Lighting energy savings calculated using climate based daylight modelling are potentially more accurate that the daylight factor assessment and combines the quality of the light (i.e. potentially glary sunlight) with qualitative parameters, the shortcoming of this method is a significantly more complicated method of evaluation. Thus, using DA and ideal dimming results in greater energy savings in comparison to EN 15193 with externally calculated DF. An ideal dimming methodology shows an increase in calculated energy savings from 5.3% to 79.5% in comparison to those obtained from EN15193. Daylight Aytonomy can be correlated to energy savings as well but still its differences with EN15193 range between -27.1% to 35.6%. It seems that EN15193 methodology is more conservative in estimating lighting energy savings than climate based daylight simulation. Similar results were obtained in another study with external obstructions [14]. The latter has to take into account the control strategy used for the activation of the shading system. 3. Conclusions There is great interest by commercial building owners in the potential impact of energy performance certificate (EPC) rating on property values and rents without the results so far support a strong correlation. Kok and Jennen [15] presented an analysis of some 1100 recent rental transactions in the Netherlands and it seems a less energy efficient building archives a 6.5% lower rent in comparison to a more efficient one. Nevertheless, although a longer time period of analysis is needed to correlate EPC ratings and revenues, any improvement on the methodology used for these ratings is welcome. In Greece , according to data from recent energy audits [16] , lighting represents the largest share of primary energy between heating and cooling and daylight exploitation is an essential strategy for increasing energy savings in office buildings. Thus the methodology used to estimate these savings will affect EPC rating and need to be accurate. From the case studies examined in this paper it seems that the national TEE-KENAK approach is totally inappropriate since lighting savings are set to a minimum value considerably downgrading building energy rating. Hourly simulations are feasible today since new daylight metrics are already used based on dynamic climatic modelling. Therefore in the effort to increase the reliability of EPCs, the adoption of a more accurate methodology for the estimation of savings due to daylight harvesting is essential. In our opinion a consensus has to be reached on the exact procedures needed to estimate EN15193:2007 parameters.
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4. References 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15. 16.
McKinsey & Company, “Unlocking Energy Efficiency in the U.S. economy”, McKinsey Global Energy and Materials Report,2009. Paolo Bertoldi,Bettina Hirl,Nicola Labanca, Energy Efficiency Status Report, European Commission Joint Research Centre Institute for Energy and Transport, 2012, ISSN 1831-9424 TOTEE 20701-1/2010, Technical Guideline “National Standards for the estimation of parameters used to calculate energy efficiency of buildings and used to issue the energy performance certificates”, 2010. Alison Williams, Barbara Atkinson, Karina Garbesi, and Francis Rubinstein, Erik Page “A Meta-Analysis of Energy Savings from Lighting Controls in Commercial Buildings”, Nov.2011, LBNL 5095E L. Doulos, A. Tsangrassoulis, F. Topalis,, “Quantifying energy savings in daylight responsive systems: The role of dimming electronic ballasts”, Energy and Buildings, Energy and Buildings, 2008, Vol. 40, , 36-50 P. Lepida, “The impact of daylight harvesting to the energy balance of office buildings”, Hellenic Open University , Msc thesis , Sep. 2014 (in greek) Michele Zinzi, Alessandro Mangione, “The daylighting contribution in the electric lighting energy uses: EN standard and alternative method comparison”, Energy Procedia 78 , 2015 , 2663 – 2668. EN15193:2007, “Energy performance of buildings — Energy requirements for lighting” DAYSIM 3.1e, http://daysim.ning.com/ DIALux, https://www.dial.de/en/dialux/download/ Rizki A. Mangkuto, “Validation of DIALux 4.12 and DIALux evo 4.1 against the Analytical Test Cases of CIE 171:2006”, LEUKOS, 2016,12:139–150. CIE 171:2006, "Test Cases for Assessment of Accuracy of Lighting Computer Programs", International Commission of Illumination, ISBN 978 3 901906 47 3 De Boer, J.; Erhorn, H., Ein einfaches Modell zur Klassifizierung der Tageslichtversorgung von Innenräumen mit vertikalen Fassaden. gi Gesundheits-ingenieur 125 ,2004, H. 6, .281-295 A. Tsangrassoulis, A.Roetzel, L. Doulos, F. Topalis, “Comparison of lighting energy savings methodologies due to daylight with EN 15193”, 3rd Int. Conference on Passive and Low Energy Cooling for the Built Environment, 29/9-1/10 2010, Rhodes, Greece. Nils Kok, Maarten Jennen, “The impact of energy labels and accessibility on office rents”, Energy Policy, Volume 46, July 2012, Pages 489-497 Greenpeace Greece, Report “Transforming current situation in the building sector using the sun as ally”, Nov. 2015 (in greek)
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