Article
Reference
Evaluating the electricity saving potential of electrochromic glazing for cooling and lighting at the scale of the Swiss non-residential national
building stock using a Monte Carlo model
CHAMBERS, Jonathan, et al.
Abstract
Novel electrochromic glazing technology has been identified as an emerging option for reducing cooling and lighting electricity demand. As this technology is particularly promising for office building we assess the related technical energy saving potential in case of nation-wide implementation in Swiss office buildings. A Monte Carlo model of Swiss office building stock using distributions of empirical building characteristics was coupled with a statistical model of energy savings of electrochromic glazing. The building stock model for Swiss office buildings was shown to produce cooling and lighting electricity demand estimates in agreement with the existing case study literature. Total yearly electricity demand for lighting and cooling was calculated to be 1152 ± 32 GWh. Electrochromic glazed saved 125 ± 6 GWh or on average 11% of lighting and cooling electricity demand. Electrochromic glazing was found to be particularly effective in highly cooled office buildings, where cooling accounted for 20 kWh/m2 and total electricity saving potential was estimated at 5.5 kWh/m2 or 5.2% of today's typical total [...]
CHAMBERS, Jonathan, et al. Evaluating the electricity saving potential of electrochromic glazing for cooling and lighting at the scale of the Swiss non-residential national building stock using a Monte Carlo model. Energy, 2019, vol. 185, p. 136-147
DOI : 10.1016/j.energy.2019.07.037
Available at:
http://archive-ouverte.unige.ch/unige:121521
Disclaimer: layout of this document may differ from the published version.
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Evaluating the electricity saving potential of electrochromic glazing for cooling and lighting at the scale of the Swiss non-residential national building stock using a Monte Carlo model
Jonathan Chambers
a*, Pierre Hollmuller
a, Olivia Bouvard
b, Andreas Schueler
b, Jean-Louis Scartezzini
b, Elie Azar
c, Martin K. Patel
aa Energy Efficiency Group, Institute for Environmental Sciences and Forel Institute, University of Geneva, Boulevard Carl-Vogt 66, 1205 Genève, Switzerland
b Solar Energy and Building Physics Laboratory LESO-PB, Station 18, 1015 Lausanne, Switzerland
c Industrial and Systems Engineering Department, Khalifa University, Abu Dhabi, UAE
*corresponding author, [email protected]
Evaluating the electricity saving potential of electrochromic glazing for cooling and lighting at the scale of the Swiss non-residential national building stock using a Monte Carlo model
Abstract
Novel electrochromic glazing technology has been identified as an emerging option for reducing cooling and lighting electricity demand. As this technology is particularly promising for office building we assess the related technical energy saving potential in case of nation-wide implementation in Swiss office buildings.
A Monte Carlo model of Swiss office building stock using distributions of empirical building characteristics was coupled with a statistical model of energy savings of electrochromic glazing.
The building stock model for Swiss office buildings was shown to produce cooling and lighting electricity demand estimates in agreement with the existing case study literature. Total yearly electricity demand for lighting and cooling was calculated to be 1152±32GWh. Electrochromic glazed saved 125±6 GWh or on average 11%
of lighting and cooling electricity demand. Electrochromic glazing was found to be particularly effective in highly cooled office buildings, where cooling accounted for 20kWh/m2 and total electricity saving potential was estimated at 5.5 kWh/m2 or 5.2% of today’s typical total electricity demand of an office building across all uses. Areas where electrochromic glazing would have particularly high potential are highlighted.
Keywords: building stock; office; lighting; cooling; electrochromic; glazing Nomenclature
Symbol Description Unit
𝐴 Gradient of 𝑝𝑟𝑜𝑏𝐶𝑂𝑂𝐿 -
𝐴𝐹𝐿𝑂𝑂𝑅 Building total floor area m2
𝐵 Intercept of 𝑝𝑟𝑜𝑏𝐶𝑂𝑂𝐿 -
𝐶 Scale constant for -
𝐶𝐷𝐷 Cooling Degree Days °C days
𝐶𝐷𝐷𝑐𝑎𝑛𝑡𝑜𝑛 Cooling Degree Days for canton °C days 𝐶𝐷𝐷𝑔𝑒𝑛𝑒𝑣𝑎 Cooling Degree Days for Geneva °C days 𝐶𝑂𝑃 Coefficient of Performance of cooling system -
𝐸𝐸𝐿 Electrical energy for cooling and lighting kWh
𝐸𝐸𝐿,𝐶𝑂𝑂𝐿 Electrical energy for cooling kWh
𝐸𝐸𝐿,𝐿𝐼𝐺𝐻𝑇 Electrical energy for lighting kWh 𝐸𝐿𝐼𝐺𝐻𝑇 𝐴⁄ Electrical energy intensity for lighting kWh/m2
𝑛 Exponent constant for 𝑃𝑇𝐻,𝐶𝑂𝑂𝐿 𝐴⁄ -
𝑁𝐹𝐿𝑂𝑂𝑅𝑆 Number of floors -
𝑁𝐻 Nominal operation hours of cooling system h
𝑁𝐻′ Adjusted nominal operation hours h
𝑃𝐸𝐿,𝐶𝑂𝑂𝐿 Electrical power for cooling kW
𝑃𝑇𝐻,𝐶𝑂𝑂𝐿 𝐴⁄ Thermal power for cooling per unit area kW/m2
𝑃𝑇𝐻,𝐶𝑂𝑂𝐿 Thermal power load for cooling kW
𝑝𝑟𝑜𝑏𝐶𝑂𝑂𝐿 Probability of a building having cooling -
SGHC Solar Heat Gain Coefficient -
1 Introduction
In 2009 the EU set itself a 20% energy savings target by 2020 [1]. In 2016 the Commission proposed an update to the Energy Efficiency Directive including a new 30%
energy efficiency target for 2030, and measures to update the Directive to make sure the new target is met [2]. In 2017 Switzerland, which has a fairly typical energy demand profile for Western Europe, set an ambitious target to reduce mean per-capita energy consumption by 43% by 2035, aiming to achieve more stringent targets than the European Union [3]. Office buildings constitute a significant fraction of Swiss energy demand, with the service sector as a whole representing 15.8% of final energy demand in Switzerland [4]. Consequently, it is an important target area for energy saving in order to meet the Swiss government targets.
Considering the increasing area of office buildings in Europe [5] and the tendency of these buildings to have high glazed fractions (glazed area over total façade area), there is a clear need for novel technologies for reducing energy demand – this is currently an unsolved technical problem. Mechanical shading systems to control solar gains present
challenges for durability and maintenance, as well as for visual comfort of building occupants [6]. Electrochromic (EC) glazing is a promising alternative, which has been identified as a potential energy saving technology [7–9]. EC glazing contains active coatings that can be reversibly switched from a clear to a coloured state by means of a small applied voltage, resulting in thermal and optical properties that can be dynamically controlled [10]. This enables dynamic adaptation to weather conditions, potentially reducing energy demand [11]. EC glazing could be more durable by being less vulnerable to weathering effects, can be more readily integrated into current buildings by direct replacement of existing windows, and can improve lighting conditions and visual comfort (e.g. maintaining views to the outside in its semi-darkened state and by avoiding glare provided the luminous transmittance in the dark state can be reduced to 0.1-1%) [12,13].
A range of research has assessed the energy saving potential from EC technology in single buildings and test cells, with values ranging from 5-30% depending on the buildings, climates, and technologies considered [8,11,14–22]. Lee et al. (2004) [11] assessed EC glazing savings potential across a range of climates in the USA, however they did not attempt to estimate the technical potential at the building stock level.
The scope of this paper includes the effect of EC glazing on electricity consumption, specifically consumption for lighting and cooling which may be most affected by EC glazing and excludes the effect on heating demand. The potential of EC glazing to reduce cooling demand is particularly interesting, as it allows to control solar gains, while climate change is expected to significantly increase cooling requirements and associated electricity demand in Europe in general and in Switzerland in particular [23]. While the effect of EC glazing on heat demand has been noted, studies disagree on whether it may increase or decrease demand [16,24]. Furthermore, the analysis of this question requires
a different modelling approach than that for lighting and cooling and is therefore excluded it from the research scope of this work.
This research aims to develop a modelling framework and building stock model that can estimate the technical potential of EC glazing for Swiss office building stock with regards to electricity saving in cooling and lighting. The technical potential is defined here as the estimated maximum energy savings in the office building stock under the assumption of 100% retrofit of existing windows. This approach places an upper bound on the potential impact of EC glazing technology in Switzerland. The calculation was performed using 2010 as a reference year, due to data availability constraints.
A Monte-Carlo (MC) simulation model was developed to estimate the yearly cooling and lighting electricity demand in office buildings recorded in the Swiss building registry on the basis of empirical distributions of building characteristics. A second model was developed to estimate the EC glazing energy saving potential as a function of building characteristics. This was used to estimate the potential national impact of the technology.
The models were developed based on the Swiss building registry (RegBL), energy label certificate database Certificat Energétique Cantonal des Bâtiments (CECB), case studies on lighting and cooling demand of several hundred buildings each, and Swiss weather data.
2 Related work
2.1 Electricity demand in office buildings for cooling and lighting
Lighting accounts for 19% of the worldwide electricity consumption and 14% of all electricity consumption in the European Union (EU) [25]. In 2016 the Swiss service sector consumed 17 TWh electricity, representing 54% of the 31.2TWh total electricity demand for Switzerland for that year [26]. Approximately 25% of this was attributed to cooling,
ventilation, and building services combined, while another 25% was attributed to lighting [27].
While national statistics are not available concerning office buildings alone, Aiulfi et al [28] performed detailed studies on 128 office buildings in Switzerland and found that overall yearly mean energy intensity (final energy consumption per unit floor area) was 105 kWh/m2, with 26.3kWh/m2 (25%) attributed to lighting, 18.9kWh/m2 (18%) to cooling, and 18kWh/m2 (17%) to ventilation. Cooling and ventilation therefore represented 34% of demand in offices, somewhat higher than the 26% reported for the service sector overall. Similar results were found by [29], showing yearly electricity consumption for cooling of 26.7 kWh/m2 for fully air conditioned buildings and 6.3 kWh/m2 for partially air conditioned ones, based on a combination of empirical and simulation results coupled with assumptions on the typical fraction of cooled floor area for office buildings.
2.2 Building stock modelling
In order to estimate the electricity demand and potential savings at national scale, a building stock model is usually used in the absence of a comprehensive dataset detailing all the relevant properties of every office building [30]. While most studies in Europe have focussed on heating demand, the methods employed remain relevant for estimating the energy demand related to cooling. At the European level, [31,32] estimated the heat and cooling demand density from population density using mean values for specific heat demand. At the city level in Switzerland, detailed heating demand simulations have been using urban models, however this approach is extremely data intensive [33,34]. [35]
developed a geo-referenced dataset of Swiss heating demand using the building registry and calibrated using the known consumption of a set of 27,000 dwellings from the Swiss
CECB (energy performance certificate) database. A similar approach was taken by [36]
to assess the heating demand of building stocks at large scale. However, in addition to addressing heating demand, these studies were only concerned with residential buildings.
In general, the diversity of the non-residential building stock has discouraged stock modelling, with only initial attempts reported in the context of the EU TABULA building stock modelling project [37]. Aebischer et al. [29] made use of a sector-based bottom-up model to estimate heat and cooling demand, using simulation results to estimate the cooling need as a function of cooling degree days (CDD).
2.3 Energy saving potential of EC glazing
There exists a range of approaches for calculating the effect of EC glazing on building performance. A number of authors considered simulations of whole office buildings using project specific designs or generic reference designs [11,15–18,38]. Other work simulated test cells or test rooms, such as a simulated office situated within a larger building [19–
21,39] or a single facade for the building [40]. These results were obtained using simulations performed with established software packages. Some papers presented experimental results performed on physical test cells, with results in line with simulations [8,14].
The research reviewed presented a wide range for the yearly energy saving potential from EC glazing from around 0% to a maximum reported value of 53%. The estimated savings depend on the technology used as a basis for comparison. In general, savings were calculated compared to classic double or triple glazing. Jestico [15] considered the largest range of glazing types, including static and dynamic shading technology, while Serra et al. considered dynamically controlled shading [22]. These comparisons highlight that the amount of energy savings depends on the baseline technology. [15] found that EC glazing
reduced energy demand by up to 20% compared to double glazing (without mechanical shading) but offered approximately no electricity savings compared to state of the art automated shading systems, as from a purely thermal perspective these two technologies have very similar effects. Nevertheless, [41] noted the role of EC glazing in bridging shading systems and glazing systems.
The savings potential also depends on the local climate, with locations with higher cooling demand presenting higher savings. Nevertheless, significant savings were reported even in locations generally assumed to have low cooling demand such as London or Helsinki [15,17,20,38]. With regards to lighting energy saving through optimised daylight use, maximum values of 50% savings in certain locations within a room have been reported, with average values around 25% [42].
3 Datasets
3.1 Description of the datasets
The building stock model was set-up on the basis of the Swiss building registry (RegBL) maintained by the Swiss Federal Office for Statistics (SFOS), using the building characteristics from the Swiss building energy label database and cooling data from previous studies in Geneva canton, lighting information from existing research, and weather data. Table 1 provides an overview of the datasets used. The RegBL aims to be a complete registry of every building and construction project in Switzerland for administrative purposes and includes for each building a unique ID (EGID). The CECB is the central repository for Swiss building energy labels and as such includes a basic description of building geometry including window and wall areas. The Geneva cooling study performed in [43] produced a database of cooling system properties from the cooling system installation certificates which are mandatory under local regulation.
Dataset Parameters used Description RegBL EGID (building identifier),
location, canton, footprint area, number of floors, building age, building category
Register of all buildings in Switzerland, 2015 version:
13,642 office buildings
CECB Window area, wall area, building age, footprint area, floor area, number of floors
Buildings energy performance certificate database including buildings up to 2017:
906 administrative buildings Geneva Cooling
study
Thermal power, electrical power and energy, COP, operating hours, location, address, cooled floor area
Cooling system permits until 2009 911 buildings
MeteoSuisse Mean daily temperature 1990-2015
Weather station data for 26 cantons
Table 1 Overview of key input datasets.
3.2 Data selection, cleaning and processing
3.2.1 Building stock data
For 14% of the 13,642 office buildings included in the RegBL, the non-residential floor areas or numbers of floors were missing (probably because certain local administrations do not make collecting this information compulsory for all office buildings). These were
‘imputed’ by replacing missing values with values randomly sampled from the existing ones, which preserves the distribution of values across the stock.
To complement data from the RegBL, information on building glazed fraction was drawn from the CECB database (see method in section 4.3). Note that the CECB is not a systematic survey of Swiss buildings since calculation of CECB is only compulsory in certain cantons. As no systematic representative survey of Swiss office buildings exist, the CECB was used as a reasonable approximation of building characteristic distributions.
3.2.2 Lighting demand data
The input distributions for lighting electricity demand intensity was drawn from [28], who noted that lighting consumption fell in line with the values expected from the SIA 2024 building regulations [44]. This study observed mean yearly energy intensity for lighting of 25.28 kWh/m2 with a standard deviation of 4.81 kWh/m2.
3.2.3 Cooling demand data
Cooling electricity demand distributions for office buildings in Geneva canton was derived from cooling system permits. Geneva canton requires an authorisation permit for all large scale cooling systems [43]. From archives of these permits for the years 1980- 2009, [43] developed a dataset of cooling system thermal power, electrical power, and nominal operation hours, and building address ID (IDPADR code). Reference files to match RegBL EGIDs with Geneva IDPADR codes were obtained from the Geneva canton geographic open data service [45]. By linking these permits to RegBL it was possible to identify 192 buildings that were classed as offices. Thermal power 𝑃𝑇𝐻,𝐶𝑂𝑂𝐿 was given for all buildings, while data was missing for 33 cases for electrical cooling power 𝑃𝐸𝐿,𝐶𝑂𝑂𝐿, 115 cases yearly electrical cooling energy consumption 𝐸𝐸𝐿,𝐶𝑂𝑂𝐿 as well as for derived variables Coefficient of Performance (COP) (eq 1) and mean yearly operating hours (NH) (eq 2).
𝑃𝐸𝐿,𝐶𝑂𝑂𝐿 = 𝑃𝑇𝐻,𝐶𝑂𝑂𝐿⁄𝐶𝑂𝑃 (eq 1) 𝐸𝐸𝐿,𝐶𝑂𝑂𝐿 = 𝑃𝐸𝐿,𝐶𝑂𝑂𝐿∗ 𝑁𝐻 (eq 2)
To fill missing values of 𝑃𝐸𝐿,𝐶𝑂𝑂𝐿 and 𝐸𝐸𝐿,𝐶𝑂𝑂𝐿, values for COP and NH were imputed by picking at random from the existing distributions. Since COP and NH were naturally bounded – COP by the physical properties of cooling heat pumps and NH by the number of hours in the year – the distributions of these two parameters contained fewer extreme values than the original values for 𝑃𝐸𝐿,𝐶𝑂𝑂𝐿 and 𝐸𝐸𝐿,𝐶𝑂𝑂𝐿, which were highly skew.
𝑃𝐸𝐿,𝐶𝑂𝑂𝐿 and 𝐸𝐸𝐿,𝐶𝑂𝑂𝐿 were calculated using COP, NH, and 𝑃𝑇𝐻,𝐶𝑂𝑂𝐿 according to eqs 1 and 2. This approach avoided the introduction of outliers. The buildings’ cooled surface area was missing from the certificate data in 37 cases. As a simplifying assumption, this area was treated as being equivalent to the total building floor area 𝐴𝐹𝐿𝑂𝑂𝑅 and missing values were filled using the building non-residential area from RegBL. Due to a lack of data, it was assumed that office cooling demand in Geneva is representative for Switzerland. Note that the non-residential floor area used did not distinguish between conditioned and non-conditioned space, since in all cases cooling was calculated as the average for the whole building.
3.2.4 Weather data
Daily temperature data was obtained from MeteoSuisse for weather stations defined in the Swiss building standard SIA380/1 [46] as being representative for each canton for the period 1990-2015. Average daily temperatures over this period were calculated and mean Cooling Degree Days (CDD) calculated with 18.3C basis temperature following the work of [47].
4 Method
A two-part Monte-Carlo (MC) simulation model was developed to estimate firstly the yearly cooling and lighting electricity demand in Swiss office buildings and secondly the energy saving potential of EC glazing for these same offices.
4.1 Office building stock electricity demand model
A model of electricity demand for cooling and lighting was developed on the basis of information on real office buildings in the RegBL. This approach aimed to associate electricity consumption and savings with physical buildings rather than economic sectors
as has been more common in previous work [29,48,49]. This was done in order to enable calculation of energy savings from glazing technology that are tied to physical building characteristics and therefore make little sense to apply to an analysis on the basis of economic sectors.
It was not possible to determine the specific per building cooling and lighting energy demand characteristics from the building registry alone, therefore, a Monte Carlo (MC) simulation was performed to assign these characteristics to the known buildings based on empirical input distributions. The dependence of the energy characteristics on the known building properties (area, number of floors, etc, see below for details) was modelled probabilistically in cases where such a dependence was significant. The total cooling and lighting electricity demand was then calculated for the simulated building stock. This process was repeated for sufficient iterations to generate a stable distribution of the energy demand totals, from which mean demand and confidence intervals were calculated. The input distributions for lighting was drawn from [28], for cooling from [43], and for other building characteristics from the CECB building certificates.
4.1.1 Lighting demand
Lighting electricity demand intensity in Swiss buildings was modelled using a normal distribution with a mean consumption of 25.28 kWh/m2 and standard deviation of 4.81 kWh/m2 based on the input data described in section 3.2.2. For each building, a value for the lighting demand intensity was drawn from the normal distribution. The total yearly demand was calculated using the building non-residential floor area, as 𝐸𝐿𝐼𝐺𝐻𝑇 = 𝐸𝐿𝐼𝐺𝐻𝑇 𝐴⁄ ∗ 𝐴𝑁𝑂𝑁𝑅𝐸𝑆𝐼𝐷𝐸𝑁𝑇𝐼𝐴𝐿.
4.1.2 Cooling demand
The cooling demand characteristics assigned for each building were:
- Presence of a cooling system.
- Thermal power demand 𝑃𝑇𝐻,𝐶𝑂𝑂𝐿.
- Electrical power demand 𝑃𝐸𝐿 using the COP (eq 1).
- Yearly electrical energy demand 𝐸𝐸𝐿 using NH (eq 2).
These characteristics were modelled using the Geneva cooling data geo-referenced to the building register, as described in section 3.2.3.
4.1.3 Cooling presence model
A model was developed which related the likelihood of cooling being used to known building properties from the RegBL building registry. To develop the model, all 1,352 office buildings for Geneva canton were selected from RegBL and linked to the cooling study data. The presence or absence of cooling could then be related to building characteristics. Figure 1 shows a strong relation between the number of floors and the percentage of buildings with cooling, while the dependence on construction period is less marked. We hypothesize that this empirical dependence reflects an underlying connection between floor number and building typology, specifically that taller buildings are more likely to be built in the ‘office tower’ style, with large non-opening windows. As a result, the correlation is more robust than e.g. total floor area, which is a highly skew and does not distinguish between large, low buildings and narrow, tall ones. Therefore, the percentage of buildings cooled was interpreted as the probability of a building being equipped with a cooling system as a function of the number of floors.
A linear regression model (eq 3) was developed for the probability of a building being cooled 𝑝𝑟𝑜𝑏𝐶𝑂𝑂𝐿, and demonstrated to be a good fit to the data (Figure 2). 𝑝𝑟𝑜𝑏𝐶𝑂𝑂𝐿 is
clipped to an upper bound of 1 in the case that 𝐴 ∗ 𝑁𝐹𝐿𝑂𝑂𝑅+ 𝐵 > 1 (e.g. for buildings with many floors).
𝑝𝑟𝑜𝑏𝐶𝑂𝑂𝐿= 𝐴 ∗ 𝑁𝐹𝐿𝑂𝑂𝑅+ 𝐵 (eq 3) 𝐴 = 0.0403 𝐵 = -0.0268
Figure 1 Percentage of office buildings equipped with cooling as a function of a) construction period b) number of floors.
Figure 2 Dependence of the percentage of buildings equipped with a cooling system as a function of the number of floors. Shaded area indicates 95% confidence interval.
4.1.4 Cooling characteristic distributions
The cooling system power and energy demand data from the Geneva study was used to develop cooling characteristic distributions and models, from which buildings could be modelled. 𝑃𝐸𝐿,𝐶𝑂𝑂𝐿 and 𝐸𝐸𝐿,𝐶𝑂𝑂𝐿 were calculated using COP and NH distributions, using a similar method to that used for imputation of missing values (see section 3.2.3). Note that the values of COP and NH were not found to be correlated to RegBL parameters within the Geneva sample (Figure 3).
Mean CDDs vary significantly between cantons, which was reflected in the model by scaling the operating hours by number of cooling degree days at the building location relative to the mean CDD for Geneva (eq 4).
𝑁𝐻′ = 𝑁𝐻 ∗ 𝐶𝐷 𝐷𝑐𝑎𝑛𝑡𝑜𝑛⁄𝐶𝐷𝐷𝑔𝑒𝑛𝑒𝑣𝑎 (eq 4)
Figure 3 Dependence of cooling characteristics of GE buildings on the building information from the RegBL. Floor area is shown on a log-scale.
It was determined that the thermal power per unit area (𝑃𝑇𝐻,𝐶𝑂𝑂𝐿 𝐴⁄ ) was dependant on total floor area according to an exponential function (eq 5), which was fit to the data (Figure 4). The area dependence could be due to efficiencies of scale, where larger cooling systems require less energy per unit area, therefore cooling characteristics were calculated according to the equation after fitting to data. Larger systems also tend to be implemented in newer buildings, hence using more advanced and efficient cooling technology.
𝑃𝑇𝐻,𝐶𝑂𝑂𝐿 𝐴⁄ = 𝐶𝐴𝐹𝐿𝑂𝑂𝑅𝑛 (eq 5) C = 0.335 n = -0.374
Figure 4 Dependence of thermal power per unit area on total floor area, with log-linear regression fit and confidence interval range. Shaded area indicates 95% confidence interval.
4.2 Total demand model
The demand was simulated according to the Monte Carlo (MC) method using the algorithm described in Figure 5. This process was repeated to generate a distribution of
cooling, lighting, and total energy demand, from which a mean estimate and confidence interval was derived.
Figure 5 Algorithm for determining the cooling energy demand for each office building.
4.3 Technical potential of EC glazing
Existing literature on the energy savings potential of EC highlighted the dependence of the magnitude of the savings on the glazed fraction (glazed area divided by total of glazed and non-glazed area, also called window-to-wall ratio) of the building. This can be seen clearly in Figure 6, which shows the change in energy savings as a function of glazed fraction.
Figure 6 Reported yearly electricity savings (due to avoided lighting and cooling) as a function of glazed fraction from a) [24] b) [15] c) [16] d) [11] e) [8] f) [19] g) [40].
Values were reported for temperate climate zones where appropriate. Mean values were calculated for cases where ranges of value were reported. Shaded area indicates 95%
confidence interval.
When selecting input data for the dependence of energy savings on the glazing fraction, it was decided to focus on the result of a single study instead of the combination of studies displayed in Figure 6, since the significant differences in approach between studies make it difficult to meaningfully combine their results into a single saving model. The results from [24] were retained (Figure 7) because it presented a number of advantages. This study used a synthetic room model instead of a whole building model, which introduced fewer additional building systems making it more appropriate for extracting synthetic savings estimates to apply to the building stock model. It provided savings estimates relative to non-shaded single glazing over a range of glazed fractions allowing a model for savings as a function of glazing to be built. This study also provided separate values for the cooling and overall savings, allowing to disaggregate to some extent the benefit from EC glazing.
The selected study used the specifications of SAGE EC glass (double glazed), U-value 1.6 W/m2.K and SHGC 0.48 in clear condition and 0.09 in coloured condition, dynamically controlled using a shading model for the ESP-r simulation software. Energy saving values were taken for simulation variants “3w” & “13w” defined in [24], corresponding to glazed fraction 0.2 and 1 respectively, with a system control target based on interior temperature of the test cell .
Linear models were generated which related the building glazed fraction to the energy savings (eq 6). For the cooling curve, the gradient is 0.313 and intercept 0.0871, while for the overall savings the gradient is 0.250 and intercept 0.0497.
𝑆𝑎𝑣𝑖𝑛𝑔(%) = 𝛼 ∗ 𝑔𝑙𝑎𝑧𝑒𝑑𝑓𝑟𝑎𝑐𝑡𝑖𝑜𝑛 + 𝛽 eq 6
Figure 7 Dependence of energy savings in cooling energy and overall per year according to [24].
A model for the building glazed fraction was developed using the CECB energy label data for administrative buildings to generate a distribution of glazed fraction using the
reported areas for window and wall elements. A significant shift in glazed fraction could be observed between pre and post 1945 buildings, effectively corresponding to the shift from ‘classic’ to ‘modern’ architectural styles (Figure 8). It was found that creating more and narrower age bands than the single pre/post war split resulted in too-small sample sets, and the difference in glazed fraction between them was no longer statistically significant. Glazed fractions for all RegBL buildings were drawn at random from either pre or post 1945 sample depending on the building’s construction year as given by RegBL.
Figure 8 Comparison of building glazed fraction distributions for buildings constructed before and after 1945.
The yearly energy saving potential for cooling and lighting was calculated using the algorithm described in Figure 9.
Figure 9 Algorithm for calculating energy savings related to EC glazing.
4.3.1 Monte Carlo confidence interval estimations
To obtain a value for the energy demand and savings values and estimate the confidence interval on these values, the combined process (algorithms from Figure 5 and Figure 9) was repeated N times to bootstrap a distribution of parameter values. For each iteration, the aggregate and per-canton electricity consumption and saving potentials were calculated. The national and cantonal mean value and confidence intervals were calculated from the resulting distributions of values.
5 Results and Discussion
5.1 Building Stock Model
The MC model was run for N=2000 iterations. The distribution of yearly electricity demand across cantons is shown in Figure 10, a log scale is used for the choropleth graph due to the highly skew distribution of energy demand results. The canton of Zurich stands
out as having particularly high energy demand, this is most likely due to the combination of the size of the canton and the high density of modern office buildings within the city of Zurich.
Figure 10 Distribution of total yearly electricity demand for cooling and lighting in Swiss office buildings by canton on a log-scale.
Figure 11 Total yearly electricity demand and yearly savings potential from EC glazing.
Table 2 and Figure 11 present the total yearly electricity demand for cooling and lighting.
The savings potential from EC glazing is also presented in these tables, these will be discussed in the following section. The total cooling demand is smaller than the lighting demand, which was expected in view of Switzerland’s temperate climate and today’s limited diffusion of office cooling. Table 3 presents the yearly electricity intensity for cooling and lighting across all office buildings. The cooling energy intensity values presented are the average per unit floor area across both cooled and non-cooled offices.
Comparing the per-unit area electricity for cooling and lighting of 29.3±0.8kWh/m2 to the 105 kWh/m2 total demand across all uses observed by the case study of [28], the stock model finds that mean cooling and lighting demand (including both cooled and non- cooled office buildings) is 28% of the total demand. The lighting demand of 24.7±0.5 kWh/m2 lies as expected within the confidence interval range of the value of 25.3 kWh/m2 measured in the aforementioned case study.
15% of buildings were assigned a cooling system by the model. As shown in Table 4 the cooling demand intensity area for these buildings was 20 kWh/m2 (19% of total) and the combined cooling and lighting demand is 44.7 kWh/m2 (43%). The demand for cooling per unit area determined by the stock model (20±0.6 kWh/m2) is within the uncertainty band of the values reported by [28,29,44] (19±9 kWh/m2, 19.2 kWh/m2, and 26.7 kWh/m2). The stock model’s good agreement with cooling energy demand estimated by independent methods suggests that the method developed is a good approximation of the Swiss office building stock.
All buildings Total (GWh)
Saving potential (GWh)
Total - Saving (GWh)
% Saving
𝑬𝑬𝑳,𝑪𝑶𝑶𝑳 180±20 27±4 160±30 15±4
𝑬𝑬𝑳,𝑳𝑰𝑮𝑯𝑻 970±20 98±2 820±20 10±0.2
𝑬𝑬𝑳 1150±40 125±6 1030±50 11±4
Table 2 Yearly electrical energy demand and EC glazing savings potential for cooling and lighting.
All buildings Total (kWh/m2/year)
Saving potential (kWh/m2/year)
Total - Saving (kWh/m2/year)
𝑬𝑬𝑳,𝑪𝑶𝑶𝑳/𝑨 4.7±0.6 0.69±0.09 4±1
𝑬𝑬𝑳,𝑳𝑰𝑮𝑯𝑻/𝑨 24.7±0.5 3.73±0.08 21.0±0.9
𝑬𝑬𝑳/𝑨 29±1 4.4±0.2 25±2
Table 3 Mean yearly electrical energy intensity and savings potential from EC glazing per unit area across all buildings for lighting and cooling. Cooling per unit area 𝑬𝑬𝑳,𝑪𝑶𝑶𝑳 is the mean of both cooled and non-cooled buildings.
Cooled
buildings Total
(kWh/m2/year)
Saving potential (kWh/m2/year)
Total - Saving (kWh/m2/year)
𝑬𝑬𝑳,𝑪𝑶𝑶𝑳/𝑨 20±0.6 3.0±0.4 16±2
𝑬𝑬𝑳,𝑳𝑰𝑮𝑯𝑻/𝑨 24.7±0.5 2.55±0.08 21.0±0.9
𝑬𝑬𝑳/𝑨 45±1 5.5±0.5 39±3
Table 4 Mean yearly electrical energy intensity only for buildings equipped with cooling (non-cooled buildings excluded from the mean).
The building stock model was able to estimate the total electricity for lighting and cooling demand with a confidence interval of 40GWh (3%). This suggests that the model outputs are reliable when aggregated to the national scale. However, while the MC bootstrapping approach enables an uncertainty estimate through the estimation of the impact of the distribution of the inputs on the distribution of the outputs, it cannot capture the uncertainties in the model input assumptions or systematic errors in input data. It is therefore likely that the model underestimates the confidence interval bands. The cooling
aspect of the model has the largest uncertainties. This is reflected to an extent in the much larger confidence interval on the cooling demand (23GWh or 11%), which is somewhat eclipsed in the total uncertainty due to the significantly lower electricity demand for cooling than for lighting.
The cooling input dataset is based on information from the canton of Geneva and may therefore miss some aspects of Swiss cooling. Nevertheless, Switzerland’s small size and relatively homogenous construction across different regions limits the impact of this drawback, as evidenced by the good agreement between the cooling stock model and the existing cooling demand values. Further validation of these results could be achieved by performing a wider survey over a range of urban and rural locations.
The lighting demand was modelled as a normal distribution, based on the consensus of building standards and field studies. Evolution in lighting technology (such as increasing prevalence of LEDs) may contribute to reducing the mean demand [18], however given that the referenced sources were relatively recent it is not expected that this would significantly affect the results in the near term.
5.2 Electricity Savings from EC Glazing
Table 2 , Table 3 and Figure 11 show the EC glazing technical potential of yearly energy savings in total and per unit floor area for cooling and lighting. These values represent an upper bound on the expected total impact of EC glazing under the assumption of 100%
replacement of existing windows with this technology. EC glazing saved on average 11%
of the electricity used for lighting and cooling (not relative to total electricity including other uses).
As we do not have total electricity demand data for office buildings, we may instead compare the electricity demand intensity results to the 105kWh/m2 mean office electricity intensity (not including heating) observed by [28]. The yearly savings potential per unit
area of 4.4 kWh/m2 represented a 4.2% saving. It is interesting to note that the share of savings from cooling overall (i.e. the mean over both cooled and non-cooled buildings) is larger than the share of total demand from cooling (Figure 12).
Considering energy savings only in office buildings equipped with cooling, the savings per unit area in this category increase to 5.5±0.4kWh/m2 due to the larger share of cooling electricity (Table 4), representing a 5.2% saving. For cooled buildings, the cooling energy demand is a significantly larger fraction relative to lighting, and the savings potential is even higher, representing more than 50% of the total potential (Figure 13). The benefit can be expected to be larger for buildings with above-average glazing fractions. The relative benefits will also depend on the reference case, as it was observed by [15] that the savings vary considerably depending on whether we compare to simple glazing or to advanced dynamic shading systems.
Figure 12 Shares of total electricity demand for cooling and lighting and shares of the total energy saving potential for cooling and lighting for the whole office building stock.
Figure 13 Shares of total electricity demand for cooling and lighting and savings for buildings equipped with cooling in the building stock.
The above results indicate that the application of EC glazing technology to the entire Swiss office building stock is unlikely to have a large total impact on a national scale, but instead provides benefits in specific cases, notably for buildings equipped with cooling.
From the building stock model, this was found to concern taller buildings - above 10 floors essentially all buildings in the input data were found to be equipped with cooling – as well as highly glazed buildings.
As Switzerland has a relatively mild climate, the benefits of adopting EC glazing on cooling demand are modest in proportion to the total electricity demand. However, this is likely to change in the future due to climate change as mean yearly CDDs are increasing by up to 10°C-days per decade [47]. Simulations on an office building in the Zurich region indicated an increase in annual cooling demand by 223-1050% in the 2050-2100 time frame [23]. It is also expected that a larger percentage of buildings will require cooling.
This would result in a significantly higher relative energy saving potential from EC glazing. Furthermore, in addition to increasing mean temperatures, the more frequent
occurrence of heatwaves could be expected to drive more demand for cooling – notably given the negative impact of extreme temperatures on health and labour productivity [50,51].
5.3 Sensitivity analysis of EC Glazing electricity savings
It can be expected that the savings results are highly dependent on the savings potential values used as an input to the model. As previously discussed, these were not modelled from a distribution of inputs because the range of studies available did not use inter- comparable methods to derive their results. To estimate the uncertainty associated with the energy savings model, a one-at-a-time sensitivity analysis was performed. The sensitivity of the total energy savings percentage to the slope and intercept of the savings model for cooling and lighting (eq 6) was evaluated by perturbing the slope and intercept coefficients described in eq 6 by ±0.05 (i.e. about 5%). The percentage energy saved in the building stock was re-calculated for each result and a linear regression was performed to determine the sensitivity coefficient with respect to each parameter. The results are shown in Table 5.
Sensitivity coefficient Slope 0.2
Intercept 1.0
Table 5 Sensitivity coefficients for to the slope and intercept of the energy savings model.
It was found that the total savings percentage varied directly proportionally with the intercept of the savings function, highlighting that this factor is the main determinant in the savings potential for EC glazing. The intercept of the model is effectively the base saving rate. Conversely, the savings were less sensitive to the change in savings as a function of glazed fraction (slope of savings model). These results show that the energy
savings from EC glazing are less influenced by the varying glazed fraction of buildings.
Instead, the overall performance of the technology across different glazed areas was more important for the Swiss case. This is likely influenced by the glazed fraction distribution of the Swiss building stock (Figure 8) - although there is a statistically significant increase in the glazed fraction for buildings constructed post-1945, this increase is relatively modest. The resulting moderate spread of glazed fractions could explain the limited sensitivity of total energy savings on the slope parameter of the savings model.
5.4 Limitations
This study made use of simplified assumptions for the savings potential of specific EC glazing (e.g. Sage glass), since there was limited available information on the current state of the non-residential building stock, the possible retrofit pathways and the potential for optimisation specific to Swiss buildings. The savings confidence intervals derived from the MC bootstrap approach therefore likely underestimate the true uncertainty in the savings. In particular, the EC electricity savings potential was taken as a single value from one study because even though a range of values are reported in literature, the diversity in underlying assessment methods precludes these from being directly combined as an input distribution. Therefore, the uncertainty around the mean electricity saving value may be significantly underestimated.
This study did not explore possible adoption or penetration rates of EC glazing in the Swiss market, rather focusing on the technical energy saving potential of this technology.
In practice, the expected real impact of EC glazing would be smaller than the technical potential, since only a fraction of buildings would be constructed or retrofitted with this technology. Additionally, the achievable savings depend heavily on what system is being
replaced and on the control strategy, as well as the optimisation target for the control strategy (energy, lighting, comfort, glare control, etc).
A number of refinements to the EC energy saving model could also be considered. Using detailed building geometry data, such as the 3D building models developed by Swisstopo [52], it may be possible to perform more detailed solar gain calculations. It may also be possible to use building element data from the CECB to develop a more detailed model of the current condition of the building stock beyond the glazed fraction that was used in this study, for example to model the current glazing types in order to better understand the impact of window replacement with EC technology.
One question that was not possible to address, but whose importance is highlighted by this study, is to what extent EC glazing systems may be able to substitute cooling systems.
This study only considered the electricity saving potential under the condition that cooling systems were installed. However, the would likely be significant potential in enabling building designs which forgo active cooling entirely. Such buildings would present the double benefit of being more energy efficient in use and having reduced lifecycle environmental impacts by eliminating the use of coolants. In addition to application at larger scale in office buildings, the avoidance of active cooling may also serve as driver for the uptake of EC glazing in the residential sector and non-office buildings in the service sector; further research would be required to estimate the related energy savings.
This study did not consider the effect of EC glazing on the heating demand of the office building stock. While existing literature on advanced glazing technologies has tended to focus on cooling energy savings, heating demand represent a significantly larger relative fraction of the Swiss total energy demand and CO2 emissions[53].
The solar heat gain coefficient in the clear state of the EC glazing commonly used in existing studies (generally based on the commercially available SAGE Glass product) is lower (0.48) than that of a standard double-glazed unit (around 0.75). Existing studies have observed that for certain combinations of glazed fraction, window orientation, and control strategy of the EC glazing increased rather than decreased heating demand by reducing the building’s solar gain in winter [18,24]. Nevertheless, under some conditions, it was possible to save energy in the heating period thanks to the dynamic optimisation of solar gains utilisation, relative to optimisation for visual comfort alone [24]. This highlights the additional challenges of modelling EC glazing for heating in the building stock and the need for future research to develop technology and control strategies that could optimise solar gains for heating. Notably, improvements to EC technology, using combined visible and near-infra red switching capabilities [40,54,55], could enable innovative approaches to minimise heating energy demand. Improved EC materials with higher clear-state transparencies would also be important to investigate [56,57].
Only energy-related benefits of EC glazing were considered, while significant benefits in terms of comfort have been suggested, notably with respect to improved lighting conditions by reduced glare and partial shading without blocking the view [22]. This would help create a more pleasant work environment with resulting benefits in terms of workplace satisfaction and employee health and productivity. EC glazing could be installed in taller buildings where they present advantages over mechanical shading in terms of resistance to harsher conditions (e.g. high wind speeds) and lower maintenance.
Historical districts represent a further application since the options for changing building’s external aspect are limited. Other non-energy benefits could include improved shading modulation with changing weather conditions and lower noise levels.
6 Conclusion
The Monte Carlo building stock model developed for this study used distributions of cooling and lighting electricity demand as a function of building characteristics to estimate cooling and lighting demand in Swiss office buildings. The total yearly electricity consumption for cooling and lighting was found to be 1152±32 GWh, with 16% (182±23 GWh) attributed to cooling and 84% (970±2 1GWh) attributed to lighting;
per unit area consumption over the entire office building stock was 29.3±0.8 kWh/m2 (4.7±0.6 kWh/m2 cooling and 24.7±0.5 kWh/m2 lighting). The model was found to be in good agreement with results from the existing literature. Topics for further research include expanding the number the building types modelled, for example to consider hotels which are large consumers of air conditioning, or to consider potential applications in the residential sector which in Switzerland has traditionally not required cooling, but which may require it in the future due to climate change.
EC glazing technical potential electricity savings for cooling and lighting were estimated to be 125±6GWh (11% of the combined demand for lighting and cooling). Per unit area savings of 4.4±0.1 kWh/m2 were found when considering the mean of cooled and non- cooled buildings. Considering only cooled buildings, the cooling demand intensity was 20±1 kWh/m2 and the savings for this type of building increased to 5.5±0.4 kWh/m2. This represents 5.2% of the 105 kWh/m2 electricity consumption across all uses (except heating) observed by [28] in their detailed study of a set of Swiss office buildings. The MC uncertainties on savings are likely to be underestimated due to limitations in the definition of the EC savings function. The sensitivity analysis noted the particularly strong effect of the intercept parameter of the savings function, which had a directly proportional effect on the resulting energy savings estimate. This could be addressed by more detailed modelling of solar gains in the building stock.
In addition to total electricity savings, electrochromic glazing can contribute to the reduction in peak power which can help avoiding overloading of the transmission grid (e.g. on hottest days of the year) as well as reduce cooling system sizes with associated cost savings [58]. A further development would be to consider future scenarios considering the impact of climate change.
The effect of EC glazing on heating energy demand was not included in the scope of this study because, as previously noted, the effect of EC glazing on heating demand is not yet well established. Further research is called for to determine the potential effect of EC glazing on heating at the scale of the office building stock. This requires taking into account the potential of EC glazing to dynamically optimise solar gains, as well as advancements in EC technologies targeted towards heating-dominated climates.
The non-energy benefits of EC glazing were not considered in this study; however, these include significant benefits over classic shading, which some authors have attempted to evaluate [59]. EC glazed windows present a range advantages including improved visual comfort, lower noise and maintenance, and a range of building integration advantages.
Such benefits may significantly increase the attractiveness of EC glazing technology for specific use cases. Technological progress in EC glazing, including improvements to transparency, dark/clear transmittance ratio, thermal properties, and the expected reduction of the time of switching from around 15 minutes today to a few minutes in future, will further increase its effectiveness without compromising the users’ comfort [60].
The higher savings for cooling in highly glazed buildings and non-energy benefits of EC glazing indicate that EC glazing should be particularly interesting for tall highly glazed buildings, where mechanical shading is not a good option. In this market segment,
especially in countries with high solar radiation and harsh outside conditions (e.g. dust, high wind speeds, pollution-related enhanced corrosion), and coupled with a range of co- benefits, EC glazing could prove and attractive technological option. Further research is however needed to improve EC technology, to allow faster switching across larger ranges, ensure improved optical properties, and explore novel approaches such as spectrally- selective switching. Finally, research should also address the question of increased electricity and peak power savings by improvements to EC technology as well as by the application of EC glazing in the residential sector.
7 Acknowledgements
This research project is financially supported by the Swiss Innovation Agency Innosuisse as part of the Swiss Competence Center for Energy Research SCCER FEEB&D.
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