Future proof assessment of NZEB houses: influence of climate change on overheating and cooling

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of climate change on overheating and cooling.

Academic year 2019-2020

Master of Science in Civil Engineering

Master's dissertation submitted in order to obtain the academic degree of ir.-arch. Jelle Laverge

Counsellors: Ir.-arch. Wolf Bracke, Ir.-arch. Kjartan Van den Brande, Prof. dr. Supervisors: Prof. dr. ir. Arnold Janssens, Dr. ir.-arch. Marc Delghust

Student number: 01403891

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of climate change on overheating and cooling.

Master of Science in Civil Engineering

Master's dissertation submitted in order to obtain the academic degree of ir.-arch. Jelle Laverge

Counsellors: Ir.-arch. Wolf Bracke, Ir.-arch. Kjartan Van den Brande, Prof. dr. Supervisors: Prof. dr. ir. Arnold Janssens, Dr. ir.-arch. Marc Delghust

Student number: 01403891

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A good building design not only provides comfort for the users, it should also be sustain-able and have a positive contribution to the environment. To achieve this, a transition to a carbon neutral building stock is necessary. I am very grateful that I got the chance to be a small part of this transition with my master’s dissertation, while combining my two biggest interests within the field of civil engineering: buildings and sustainable design. Therefore, I would like to thank my supervisors and counsellors for their advice during my master’s dissertation. They gave me the perfect combination of freedom to do my own analysis with their guidance when necessary. Special thanks to my family and friends for their help and support during the year.

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”The author(s) gives (give) permission to make this master dissertation available for con-sultation and to copy parts of this master dissertation for personal use. In all cases of other use, the copyright terms have to be respected, in particular with regard to the obligation to state explicitly the source when quoting results from this master dissertation.”

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houses: influence of climate change

on overheating and cooling

by

Elisa Vandenbussche

Master’s dissertation submitted in order to obtain the academic degree of Master of Science in Civil Engineering

Supervisors: Arnold Janssens, Mark Delghust

Counsellors: Wolf Bracke, Kjartan Van den Brande, Jelle Laverge Faculty of Engineering and Architecture, Ghent University

Abstract

The Energy Performance of Buildings (EPB) regulations aim to reduce primary energy consumption and carbon dioxide emissions of buildings, without users losing comfort. The most important parameter in the EPB calculations is the primary energy indicator (E-level), which is a measure for the global energy performance of a building. In this study, the influence of climate change on the EPB calculations, and more specifically on the risk of overheating and excessive cooling in buildings, is analysed. This is done by analysing the calculation method of the E-level, to find out which parameters may change due to climate change. In order to predict how the climate parameters will change, climate projections are discussed and the possible simulation software that can be used. Meteonorm is concluded to be very suitable and the simulations are done for the average and 10-year monthly extreme temperature and radiation values in 2050 for Uccle. These future climates are implemented in a Revit- and Excel-based tool that calculates the different steps of the E-level for six different building typologies, apartments with 1 or 2 bedrooms, closed and semi-detached buildings with 3 bedrooms and detached buildings with 1 or 3 bedrooms. Two cost-optimal packages and two passive house packages are used for the characteristics of the building envelope and the technical installations, and two multi-zone heating user profiles are used. The E-level, the overheating indicator and the net energy for heating and cooling is analysed. The influence of climate change on the E-level depends on the package of measures. For the cost-optimal packages, the decrease in energy for heating in the future has more impact than the increase in energy for cooling, resulting in a reduction of the E-level. The E-level of the passive house packages will in general increase, as the energy for cooling has more impact for these cases than the energy for heating. In the current climate, it is rather exceptional that the cooling energy is higher than the heating energy in buildings, this is only the case for the apartments with one of the passive house packages. For the climate in 2050 with the maximum values, all buildings with passive house packages have higher energy needs for cooling than for heating, with as result that some less isolated buildings become more energy efficient. Keywords: climate change, Energy Performance of Buildings, primary energy indicator, overheating indicator, Meteonorm

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Future proof assessment of NZEB houses: influence

of climate change on overheating and cooling

Elisa Vandenbussche

Arnold Janssens, Marc Delghust, Wolf Bracke, Kjartan Van den Brande, Jelle Laverge

Abstract— The Energy Performance of Buildings (EPB)

regulations aim to reduce primary energy consumption and carbon dioxide emissions of buildings, without users losing comfort. The most important parameter in the EPB calculations is the primary energy indicator (E-level), which is a measure for the global energy performance of a building. In this study, the influence of climate change on the EPB calculations, and more specifically on the risk of overheating and excessive cooling in buildings, is analysed. Besides this, the best way to project the future climate is also studied. The future climate projections are done with Meteonorm, resulting in an average and extreme climate for 2050 in Uccle. These are used as climate input in a Revit- and Excel-based tool that calculates the overheating indicator, the net energy for heating and cooling and the E-level for different building typologies. The influence of climate change on the E-level depends on the package of measures of the characteristics of the building envelope and the technical installations. For the cost-optimal packages, the decrease in energy for heating in the future has more impact than the increase in energy for cooling, resulting in a reduction of the E-level. The opposite is true for the passive house packages, with as result that some typologies with cost-optimal packages are more energy-efficient in the future climates than some passive-house buildings. Besides this, all buildings with passive house packages have higher energy needs for cooling than for heating in the extreme future climate, with as result that some less isolated buildings become more energy efficient.

Keywords— climate change, Energy Performance of

Buildings, primary energy indicator, overheating indicator, Meteonorm

I. Introduction

This study takes place within the wider NEPBC-project. The project is fully called ‘Next generation building energy assessment methods towards a carbon neutral building stock’. The goal of the project is to develop the framework of future regulations of the Energy Performance of Buildings (EPB), to support the transition to a carbon neutral housing stock in 2050. The EPB regulations aim to reduce primary energy consumption and carbon dioxide emissions of buildings, without users losing comfort. The most important parameter in the EPB calculations is the primary energy indicator (E-level), which is a measure for the global energy performance of a building and makes it possible to compare energy performances of buildings in an objective way. The lower the E-level, the more energy-efficient the building is [1].

For the calculations of the E-level, assumptions about the climate in the environment of the buildings are necessary. The current EPB calculations use fixed climate variables that did not change since the regulations are in place, that is since 2006. In this study, the influence of climate change on the EPB calculations, and more

specifically on the risk of overheating and excessive cooling in buildings, is analysed. This is done by analysing the calculation method of the E-level, to find out which parameters may change due to climate change. In order to predict how these parameters will change, climate projections are discussed and the possible simulation software that can be used. Meteonorm is concluded to be very suitable to predict the future climate on different locations in Belgium in a convenient way. The EPB calculations are done with a Revit- and Excel-based tool created by Ghent University, in which the climate input can be changed to study the influence on the overheating and cooling.

II. E-level

The primary energy index is formed by the ratio of the characteristic yearly primary energy use (Echar ann prim en cons) and a reference value. The formula for the primary energy index (E) is given below.

E= 100 ∗ Echar ann prim en cons Echar ann prim en cons, ref

(1) The characteristic yearly primary energy use

Echar ann prim en cons is the sum over all the months of the monthly primary energy need for space heating, domestic hot water and auxiliary devices and the equivalent monthly primary energy need for cooling, subtracted by the monthly primary energy production of photovoltaic solar energy systems and property-bound co-generation systems. The reference value of the characteristic yearly primary energy use Echar ann prim en cons, ref is determined by the total area of all the separation structures that surround the EPW unit and through which transmission losses are considered for the determination of the energy performances, the total volume of the EPW unit and the reference hygienic ventilation flow rate of the EPW unit [2].

In this study, only the influence of climate change on the energy need for space heating and cooling is studied. Both are calculated by means of the heat losses due to transmission and ventilation, that depend on the monthly mean outdoor temperature, and the heat gains due to radiation, that depend on the total and diffuse monthly radiation on a horizontal shadowed plane. The outside temperature and radiation used for the calculations are fixed but will evolve in the future. The heat gains are also influenced by the parallel sunshading and shadowing of the windows. The threshold for the need of cooling

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overheating indicator is higher than 6500 Kh, cooling is necessary and a cooling installation should be installed. For an overheating indicator lower than 1000 Kh, cooling is not necessary. In between these thresholds, a linear interpolation is done for the probability that a cooling installation will be needed in the future, resulting in fictitious cooling [2].

III. Climate projections

The emission scenarios of the IPCC are used in climate modelling to make projections that represent a range of possible futures. The greenhouse gas emissions of the different scenarios are used as input of Global Climate Models. These Global Climate Models are modelling tools that represent the physical processes in the atmosphere and oceans and are used to project future climate change. These climate models consist of a grid around the Earth. The resolution of this grid is coarse, around 100 to 300 km, which makes it difficult to project local weather, and they do not represent the extreme values well. Therefore, the outputs of these Global Climate Models are further downscaled [3]. An example of this is shown in Figure 1, where the evolution from the large scale Global Climate Models to the local scale Regional Climate Models is shown.

Fig. 1: From Global Climate Model to Regional Climate Model [4]

The downscaling can be done in three ways. A first option is to use dynamical downscaling. That means that a Regional Climate Model is used, which can be seen as a smaller and more precise version of a Global Climate Model. The second option is the use of statistical downscaling. Statistically downscaling is a time series transformation tool, by which higher spatial and temporal resolutions can be derived. There are three different methods: weather generators, regression methods and weather classifications. Besides statistical and dynamical downscaling, also combinations of these two are possible which are called hybrid downscaling [5]. An overview of

Fig. 2: Overview climate projection methods IV. Meteonorm

The downscaling methods result in weather files, which can be used in Building performance simulation (BPS) or EPB calculations. In this study, Meteonorm will be used to produce these output files. Meteonorm is a stochastic weather generator, but besides that it is also a climatic database and spatial interpolation tool. The database has access to more than 8000 weather stations, five geostationary satellites and a globally calibrated aerosol climatology. This combination makes it possible to generate accurate and representative typical years for any place on earth for more than 30 parameters [6].

Besides a data provider, Meteonorm contains also algorithms to calculate extreme years and the urban heat effect and can be used for climate change studies. Thanks to the spatial interpolation tool, data is not only available at weather stations. Solar radiation, temperature and additional parameters can be retrieved for any place in the world.

With Meteonorm, it is possible to simulate the outdoor air temperatures and horizontal total and diffuse radiation needed for the EPB calculations for slices of 10 years in the future. From these time-periods of 10 years, one reference year is made. In this study, the Test Reference Years will be used. The SRES climate scenario A2 of the IPCC will be used for the simulations. The mean monthly outdoor temperature and radiation in Uccle for 2050 will be used as representative climate data, both the average and 10-year monthly maximum values. In what follows, these will be called the average climate and the maximum climate. These maximum values can be representative for an extreme hot summer or the higher temperatures in big cities due to the urban heat island effect. The increase in mean yearly outside air temperature compared to the currently used EPB climate is +1.2 °C for the climate with average values and +2.4 °C for the climate with maximum

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Fig. 3: Uccle 2050 average and Uccle 2050 maximum compared to EPB climate for (a) temperature, (b) total radiation and (c) diffuse radiation

and overheating, a Revit- and Excel-based tool of Ghent University will be used. This tool makes it possible to calculate the different steps of the EPB calculation method for the E-level for different building typologies and packages of measures that are representative for the building stock. Besides this, it is also possible to adjust the standard values of the EPB calculation method to do scenario-analyses, and even to use multi-zone calculation methods for heating instead of the single-zone calculation method of the normal EPB calculations [7].

The above discussed temperatures and radiations will be implemented in the tool as climate input. Not only the climate has an influence on the cooling and overheating in buildings. These are also influenced by the availability and characteristics of the sunshading, the shadowing of the building and the window percentages. For the sunshading, the main options are an exterior sunshading, an interior sunshading, a sunshading in between the window glasses or no sunshading at all. In case of a sunshading, it can either be manual or automatically controlled. The shadowing of the building will remain the same as in the original EPB calculations. The window percentages are calculated with the total area of the windows and the total area of the building envelope.

The six typologies of buildings that are made in Revit are apartments with 1 or 2 bedrooms, closed and semi-detached buildings with 3 bedrooms and detached buildings with 1 or 3 bedrooms. Of each typology, 1500 different buildings are modelled, except for the detached 1 buildings there are only 671 buildings. Of the eight available packages of measures in the tool for the characteristics of the building envelope and the technical installations, the cost-optimal packages MP3 and MP4 are used and the passive house packages MP5 and MP6 [8]. The EPB calculations use single-zone heating with a constant set-point temperature of 18 °C for all the rooms. In this study, two multi-zone heating user profiles are used. User profiles 1 assumes a constant set-point temperature of 21 °C for 24 hours in all the rooms. User profile 4 assumes that all the rooms, except the garage, are heated for only 5 hours. The bathroom is heated to 22 °C while the temperature in the living room and the kitchen is set on 21 °C. The set-point temperature in the bedrooms, circulation rooms and toilet is 17 °C. These two user profiles give the maximum and minimum amount of heating of the seven user profiles that are included in the tool.

VI. Influence of climate change on overheating and cooling

A. Influence on E-level

When the E-level is calculated for the different typologies, MPs and climates according to the current single-zone heating method of the EPB calculations, it can be concluded that the influence of the climate on the E-level depends on the package of measures. The median values

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TABLE II: Percentages of buildings with Ioverh ≥ 6500 Kh

of the E-levels for the four MPs and all the typologies are shown in Table I. The cases for which the difference in E-level between the EPB climate and the maximum climate is 3 or more are shown in green for a decrease and in red for an increase. The decreases are for all typologies of MP3, for the single-family houses of MP4 and for the closed and semi-detached buildings of MP6. The increases are the highest for the apartments with MP5 and MP6 and for the detached 3 buildings with MP5. If the E-level decreases due to the changing climate, this means that the decrease in energy for heating is higher than the increase in energy for cooling for the future climate, resulting in a total decrease of energy demand. The maximum decrease in E-level occurs for the closed buildings for the maximum climate and equals -11 for MP3 and -7 for MP4. Due to a higher increase in energy for cooling than the decrease for heating, the E-levels can increase for the future climates. For the apartments with MP6, there is a small increase for the average climate (of 2 and 3) and a very big increase for the climate with maximum values, with a difference of 13 and 16 for respectively apartment 1 and 2.

For the current EPB climate, the E-levels of MP5 and MP6 are lower than those of MP3 and MP4. Due to the decrease in E-level of the single-family houses with MP3 and the increase for MP5, the E-levels of both cases are nearly the same in the climate with average values and are even lower for MP3 than MP5 in the climate with maximum values, with differences between 2 and 10. The E-levels for the single-family houses of MP4 are much higher than the other MPs and those of MP6 are much smaller. For the apartments, the E-levels for MP4 are comparable to those of MP5 (maximum difference of 3) and are a lot lower than the median E-levels of MP3 and MP6. Looking at the absolute values of the E-levels, it can be seen that the differences between the E-levels of

the apartments and those of the closed, semi-detached and detached buildings is very high for MP3 and MP6. A logical explanation is the presence of photovoltaic solar systems for these packages of measure, that are only placed for the single-family houses and not for the apartments. Based on the E-level, the single-family houses with MP6 are the most energy-efficient, both for the current and the future climates. For the apartments, MP5 is the most energy-efficient in the current climate and MP4 and MP5 are the same for the maximum climate.

B. Influence on overheating indicator

An overview of the percentage of buildings for which the overheating indicator is higher than the maximum threshold value of 6500 Kh is given in Table II. The majority of the apartments with MP4 or MP5 (from 52 to 100 %) and a minority of the detached 1 buildings (33 and 23 %) with MP5 or MP6 need cooling for the EPB climate. In the average climate, these amounts only increase. Besides the apartments and detached 1 buildings, also 13 to 32 % of the detached 3 buildings and 71 to 96 % of the semi-detached buildings need cooling for MP5 and MP6. For the maximum climate, cooling is needed for nearly each typology and MP, only the closed and detached 3 buildings with MP3 or MP4 will not need cooling.

C. Influence on energy for heating and cooling

The cooling and heating of the different buildings are calculated with user profile 1 and user profile 4 for multi-zone heating. The window percentage of a building envelope influences the amount of heating and cooling. The behaviour in function of the window percentage is the same for the packages of measures, but differs for the typologies for the average climate in 2050. For apartments, the sum of net heating and cooling stays nearly the same

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for different window percentages. This is because higher window percentages not only result in more cooling, but also in a decrease of heating. For the single-family houses, the heating does not decrease in function of the window percentage. The cooling does increase, resulting in an increase of net energy for heating and cooling. The rise in cooling in function of the window percentage increases from closed to semi-detached to detached houses. This means that the total energy is mostly influenced by the window percentage for detached buildings and least for closed buildings and apartments.

The median of the energy for cooling and heating for all the building cases is also calculated. The median energy for cooling increases in the average climate with 23% to 1839% compared to the EPB climate. The least increase is for the apartments with MP5, the highest increase is for the apartments with MP3. The extreme cases are the same for the maximum climate, but with increases of 75% and 11945%. The decrease in energy for heating ranges between -13% and -27% for the average climate, and between -37% and -71% for the maximum climate for user profile 1. The decrease is higher for user profile 4, with differences of -16% until -40% for the average climate and between -42% and -93% for the maximum climate. The decrease is the lowest for the detached 1 buildings with MP3 and the highest for the apartments 1 with MP6.

D. Influence on net sum of heating and cooling

Just as the E-level, the sum of net energy for heating and cooling increases for the average climate for MP5 and

MP6, and decreases for MP3 and MP4, with only a few exceptions. The median values of the sum of net energy of heating and cooling are shown in Table III, both for user profile 1 and 4. The closed buildings with MP3 and MP4 are the most robust buildings based on their distribution, which means that climate change will result in the highest decreases of the net energy and the smallest variation between the net energy of the buildings compared to the other typologies. The detached 3 buildings with MP5 and MP6 are the least robust design. The behaviour is different for the maximum climate. Only the single-family houses with MP3 and MP4 have a decreasing net energy. The net energy of heating and cooling increases for the apartments with MP3 and MP4, just as for all typologies with MP5 and MP6. The most robust design is still the closed buildings with MP3 and MP4, but the apartments with MP6 have the least robust design, followed by the closed buildings with MP5 and MP6.

In Table III, the cases for which the less isolated packages MP3 or MP4 have a lower net energy than MP5 are shown in green, when they are also lower than MP6 they are dark green. For the average climate, it can be concluded that the packages MP3 and MP4 result in a lower energy use for heating and cooling for the apartments than MP5. For the single-family houses with user profile 4, the energy use of MP4 with sunshading is only slightly higher than that of MP5, and lower for the detached 3 buildings. For the maximum climate, the median net energy use of MP4 with sunshading is always lower than that of MP5. For the apartments and closed buildings, the energy for heating

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for which the rooms are heated 5 hours a day. In case all the rooms of the buildings are heated 24 hours a day to 21 °C, heating is still more important than cooling in the future climates with only one exception of closed buildings with MP4 in the maximum climate.

VII. Conclusion

There are a lot of methods to predict climate change. Meteonorm can be a good choice for Building Performance Simulations, as it combines climate projection with different IPCC scenarios, GCMs and downscaling, it has its own database and it generates both monthly and hourly values. Due to the spatial interpolation, it is possible to obtain results for every location in Belgium. The projected future climates of Meteonorm are used in a Revit- and Excel-based tool of Ghent University to study the influence of climate change on the EPB calculations.

The future climates used in this study are the average and the 10-year monthly maximum values in Uccle for 2050, called the average climate and the maximum climate. These maximum values can be representative for an extreme hot summer or the higher temperatures in big cities due to the urban heat island effect. The increase in mean yearly outside air temperature compared to the currently used EPB climate is +1.2 °C for the average climate and +2.4 °C for the maximum climate.

Climate change not only has an influence on the absolute value of the E-level, it also influences the package of measures with the lowest E-level. MP5 has a lower E-level than MP1 in the current climate, but in the future climates the E-level of MP3 is lower with differences between 2 and 10 for the maximum climate. Although the E-level of the apartments with MP6 is in the same range of those for the other MPs for the current climate, the E-levels become much higher in the maximum climate, with differences of 12 and 14 compared to MP4 and MP5.

When the absolute values of the sum of net heating and cooling are compared, it can be concluded that the less isolated packages of measures MP3 and MP4 can be more energy efficient in the future than the well isolated MP5 and MP6 houses, especially if MP3 and MP4 are combined with exterior sunshading. This is already the case in the average climate for the apartments, and can be the case for the closed and detached 3 buildings in the maximum climate. This is a direct result from the fact that cooling of buildings will become more important than heating in the future. In the current climate, it is rather exceptional that the cooling is higher than the heating in buildings, this is only the case for apartments with MP5. For a temperature increase of 1.2 °C, this becomes the case for all apartments with MP5 and MP6 and the detached houses with MP5 and MP6 with user profile 4. For a temperature increase of 2.4 °C, all buildings with MP5 and MP6 have higher energy needs for cooling than for heating, with as result that some less isolated buildings become more energy efficient.

EPB. [Online]. Available: https://www.energiesparen.be/ bouwen-en-verbouwen/epb-pedia

[2] ——, “EPB bijlage V - bepalingsmethode EPW,” p. 209. [3] Koninklijk Nederlands Meteorologisch Instituut,

Ministerie van Infrastructuur en Milieu. KNMI weer- en klimaatmodellen. [Online]. Available: https://cdn.knmi.nl/system/about blocks/files/000/000/004/ original/NL KNMI weer- en klimaatmodel A4.pdf?1496827577 [4] Q. P. T. T. M. W. P. Siwila, S., “Climate change impact

investigation on hydro-meteorological extremes on zambia’s kabompo catchment,” p. 13.

[5] United States Agency International Development, “Center for international earth science information network (CIESIN),” vol. 45, no. 5, pp. 45–2621–45–2621.

[6] Meteotest AG. Meteonorm features. [Online]. Available: https: //meteonorm.com/en/meteonorm-features

[7] Y. D. Delghust, Marc and A. Janssens., “Improving the predictive power of simplified residential space heating demand models : a field data and model driven study,” p. 316.

[8] L. J. A. J. L. C. P. F. P. L. B. J. Bracke W., Delghust M., “Studie EPB-eisenpakket residenti¨ele nieuwbouw,” p. 423.

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A Ratio of energy for cooling and energy for heating 116 B E-level for the different climates 123 C Overheating indicator for the different typologies 127 D Energy for cooling and heating in function of the window percentage 131 E Sum of net energy for heating and cooling for user profile 1 156 F Sum of net energy for heating and cooling for user profile 4 162

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List of Acronymes

AR5 Fifth Assessment Report ATL Adaptive temperature limit AWD Ambient warmness degree

BPS Building performance simulation

CDD Cooling degree day

CMIP Coupled Model Intercomparison Project

DHW Domestic hot water

DSY Design Summer Year

ECY Extreme Cold Year

EPB Energy Performance of Buildings

EPW EnergyPlus Weather

ESM Earth System Model

EWY Extreme Warm Year

FS Finkelstein-Schafer

FTL Fixed temperature limit

GCM Global Climate Model

HDD Heating degree day

IOD Indoor overheating degree

IPCC Intergovernmental Panel on Climate Change KNMI Royal Netherlands Meteorological Institute

MP Package of measures

RCM Regional Climate Model

RCP Representative Concentration Pathway RMI Royal Meteorological Institute

SRES Special Report on Emissions Scenarios TDY Typical Downscaled Year

TMY Typical Meteorological Year

TRY Test Reference Year

UMY Untypical Meteorological Year XMY Extreme Meteorological Year

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Chapter 1 Introduction

This study takes place within the wider NEPBC-project. The project is fully called ‘Next generation building energy assessment methods towards a carbon neutral building stock’. The goal of the project is to develop the framework of future regulations of the Energy Performance of Buildings (EPB), to support the transition to a carbon neutral housing stock in 2050. The EPB regulations aim to reduce primary energy consumption and carbon dioxide emissions of buildings, without users losing comfort. The most important parameter in the EPB calculations is the primary energy indicator (E-level), which is a measure for the global energy performance of a building and makes it possible to compare energy performances of buildings in an objective way. The lower the E-level, the more energy-efficient the building is.

For the calculations of the E-level, assumptions about the climate in the environment of the buildings are necessary. The current EPB calculations use fixed climate variables that did not change since the regulations are in place, that is since 2006. In this study, the influence of climate change on the EPB calculations, and more specifically on the risk of overheating and excessive cooling in buildings, is analysed. This is done by analysing the calculation method of the E-level, to find out which parameters may change due to climate change. In order to predict how these parameters will change, climate projections are discussed and the possible simulation software that can be used.

In a first chapter, the EPB calculations are discussed in detail, with the focus on the parameters of the energy calculations in buildings that are directly influenced by climate change, such as the heat gains, the heat losses and the overheating indicator. The most important climate variables that influence these calculations are the monthly average out-side temperature and the monthly average solar radiation. To know how the temperature and radiation will evolve due to climate change, climate projections are necessary. The climate change in a certain region depends on the global climate change of the Earth. The Intergovernmental Panel on Climate Change (IPCC) does a lot of research about the possible future climate scenarios, which are based on the changes in greenhouse gas emissions, aerosol and land cover. The future climate scenarios of the IPCC are discussed

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in chapter 3 and used in climate modelling to make projections that represent a range of possible futures.

Climate projections are a way to predict the future climate. This is done by climate mod-els, which are modelling tools that represent the physical processes in the atmosphere and oceans. With the climate scenarios of the IPCC as input for these climate models, future weather can be predicted. The output of the climate models are climate variables that can be used in building performance simulation, such as outdoor temperature, radiation, wind speed, humidity, .... In this study, Meteonorm will be used as a climate projection method. The possible climate models, projection methods, the resulting output files and the reason Meteonorm is chosen for this study, are discussed in chapter 4.

To cover the whole range of possible future weather, different scenarios can be made. These scenarios can exist of average values or extreme values of the climate variables and can be representative for different situations and locations. Two examples of such scenarios that were made for The Netherlands and Belgium are discussed in chapter 5 and the climate differences in Belgium are analysed. There is already a lot of research done about the influence of climate change on building performance simulation, such as the increasing overheating risk in buildings and the higher cooling demands in the future. A few of these studies are discussed in chapter 6.

The influence of climate change on the EPB calculations, and more specifically on the overheating and cooling, is simulated in this study with a Revit- and Excel-based tool created by Ghent University. With this tool, it is possible to analyse the different com-ponents in the calculation of the E-level for different building typologies, packages of measures and heating user profiles, for different climate inputs. The future climate in Belgium is projected with the weather generator Meteonorm and the resulting values of the temperature and radiation are used as climate input in the Revit- and Excel-based tool. The tool itself and its possibilities are discussed in chapter 7 and the future climate in Belgium that results from the simulations in Meteonorm is discussed in chapter 8. With the output of the Revit- and Excel-based tool, the change in E-level, the increase of the overheating indicator, the comparison of heating and cooling for different window percentages and the change in sum of net heating and cooling in the future is calculated. This can be done for six different building typologies, eight packages of measures, seven possible user profiles for multi-zone heating and multiple combinations of parallel sun-shading of the windows. Besides this, there are also different future decades possible with average, extreme or urban heat values, for different IPCC climate scenarios on different locations. As this results in a huge amount of possible scenarios, first a selection of rep-resentative scenarios is made in chapter 9, after which the results for these scenarios are discussed in chapter 10.

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Chapter 2 EPB overview

2.1 General

This study takes place within the wider NEPBC-project. The project is fully called ‘Next generation building energy assessment methods towards a carbon neutral building stock’. It is a cooperation between Ghent University, the University of Leuven, Scientific and Technical Construction Centre (WTCB) and Pixii (platform for energy neutral building), and coordinated by Ghent University. The project started in 2017 and should be finished in 2021.

The goal of the project is to develop the framework of future regulations of the Energy Performance of Buildings (EPB), to support the transition to a carbon neutral housing stock in 2050. This by meanwhile assessing the techno-socio-economic impact at the individual building, the local grid and the building stock level [1]. To do so, buildings will have to fulfil strict envelope requirements and be equipped by complex systems. Therefore, the EPB regulations will be very important.

The EPB regulations aim to reduce primary energy consumption and carbon dioxide emis-sions of buildings, without users losing comfort. The regulations are in place since 2006. They impose requirements for insulation, ventilation and overheating with the aid of dif-ferent indicators. The most important indicator is the primary energy indicator (E-level), which is a measure for the overall energy performance of a building and makes it possible to compare energy performances of buildings in an objective way. The lower the E-level, the more energy-efficient the building. The different parameters and their contribution to the E-level, together with the calculation method, are discussed in paragraph 2.3.

Besides the indicator for overall energy performance some other requirements were set, some of which have been removed or introduced in the past. These requirements focus on the performance of the building envelope, the energy efficiency of the installations and the quality of the indoor climate. An overview of the requirements is shown in Figure 2.1. In the following paragraph, the indicators and their requirements will be discussed more in detail.

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Figure 2.1: Overview EPB-requirements

2.2 Requirements

Not every building has to fulfil the EPB-regulations. There is an obligation for buildings that are climatized, which means they are heated or cooled, and those for which an environmental license for urban planning is requested. Buildings in which people live, work, sport, shop, sleep, are cared for, ... and are not industrial or agricultural buildings are always climatized according to the energy performance regulations, which means that they have to fulfil the EPB-regulations. The requirements also depend on the nature of the works (new building, renovation or major energetic renovation) and the destination of the building (residential, non-residential, industry, agriculture or common parts). The requirements discussed in the following paragraphs are only for new residential buildings. A distinction will be made between a building unit, which is for example an apartment, and the whole building, which is for example the whole apartment building [2].

The requirements that have to be fulfilled depend on the year the building request is submitted. To smouthen the transition towards a carbon neutral building stock the requirements become stricter over the years. For example, in 2006 the thresholds for the E-level was set on E100. This made sure that stricter E-level thresholds in future years are easily relatable to this first threshold. In 2020 the threshold is set to E35. This means that on average new buildings are performing 3 times better than their predecessors in 2006.

2.2.1 R- and U-value

The thermal resistance of materials (R-value) depends on the thickness of the material and its thermal conductivity and is a measure for the ability of the material to retain heat. The thermal transmittance (U-value) is inversely proportional to the total thermal resistance of an element and is an indication of how good an element is insulated. It represents the amount of heat transmitted through a building element, divided by the area and temperature difference between the two sides of the element. The EPB-regulations give values for the maximum allowable U-values. In 2006, the maximum U-values for windows, roofs and walls that are not in contact with the ground were respectively 2.5, 0.4 and 0.6 W/m2K. In 2020, the maximum U-values for the same elements are 1.5, 0.24 and 0.24

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W/m2K.

2.2.2 K-level

Unlike the U-values, that give an indication of the insulation of one element in a building, the K-level gives an indication of the insulation level of the whole building. Therefore, it depends on the heat losses through both the walls and windows as well as the floors and roofs. The K-level is calculated by the ratio of the mean thermal transmittance of the building Um and a reference value Um,ref, multiplied by 100. The lower the K-level, the

better the building is insulated. Besides the U-values of the individual elements, also the compactness and heat loss through thermal bridges influence the K-level. Currently, the K-level is only used for industrial buildings. In 2018, the K-level for residential buildings was replaced by the S-level [2].

2.2.3 S-level

The K-level only gives an indication of the quality of insulation, but gives no information about the energy demand for heating and cooling. This drawback does not apply to the S-level, that summarizes all the energetic qualities of the building envelope into one value. This means that it not only assesses the degree of insulation of the building envelope, but also the airtightness, sunshading, the size, orientation and type of windows, overheating, the solar gains and the form efficiency. The less energy that is needed to keep the temperature inside the building unit steady, the lower the S-level. Just as the E-level, the S-level applies on a building unit, not on the whole building [2].

2.3 E-level calculation

The Energy Performance of Buildings (EPB) can be quantified by a primary energy indi-cator (E-level). This E-level makes it possible to compare the performances of buildings in an objective way. To know in which way the EPB will change under influence of a chang-ing climate, the calculation method of the overall primary energy indicator is observed in detail. The building is divided into partial volumes for the calculations, EPW units, that each on their own should fulfill the energy performance requirements. If necessary, a further subdividing is done into ventilation zones and energy sectors, to calculate with different types of installations. There are five steps in the calculation of the E-level [3]: 1. Calculation of the monthly net energy need for space heating and domestic hot water (DHW)

2. The monthly net energy need is divided by the system efficiencies to get the monthly gross energy need

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3. The monthly final energy use for space heating and DHW is determined, just as the monthly final energy use for auxiliary devices and cooling and the characteristic monthly energy production of photovoltaic solar energy systems or property-bound co-generation systems.

4. Each of the monthly final energy uses is multiplied by their corresponding primary energy factor and summed up over the different energy sectors, resulting in the monthly primary energy needs.

5. The characteristic yearly primary energy use Echar ann prim en cons is calculated, by a

summation of the monthly primary energy needs over the months. The self-generated energy is subtracted from this value.

6. The primary energy indicator E is calculated by dividing the characteristic yearly primary energy use by a reference value.

The ratio of the characteristic yearly primary energy use (Echar ann prim en cons) and the

reference value (Echar ann prim en cons, ref), resulting in the E-level, is shown in formula (2.3.1).

E = 100 ∗ Echar ann prim en cons Echar ann prim en cons, ref

(2.3.1)

Echar ann prim en cons = 12

X

m=1

(Ep,heat,m+ Ep,water,m+ Ep,aux,m+ Ep,cool,m− Ep,pv,m− Ep,cogen,m)

(2.3.2) with

Ep,heat,m: the monthly primary energy need for space heating

Ep,water,m: the monthly primary energy need for domestic hot water

Ep,aux,m: the monthly primary energy need for auxiliary devices

Ep,cool,m: the equivalent monthly primary energy need for cooling

Ep,pv,m: the monthly primary energy production of photovoltaic solar energy systems

Ep,cogen,m: the monthly primary energy production of property-bound co-generation

sys-tems

The reference value of the characteristic yearly primary energy use Echar ann prim en cons, ref is

determined by the total area of all the separation structures that surround the EPW unit and through which transmission losses are considered for the determination of the energy performances, the total volume of the EPW unit and the reference hygienic ventilation flow rate of the EPW unit. In this study, only the influence of climate change on the energy need for space heating and cooling is studied. Therefore, only the monthly primary energy needs for those two will be discussed in detail. For both of them, the monthly net energy need will be discussed first in the paragraphs 2.3.1 and 2.3.3, together with the parameters that are influenced by the climate. After that, the monthly primary energy needs are discussed in the paragraphs 2.3.2 and 2.3.4.

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2.3.1 Monthly net energy need for space heating

The monthly net energy need for space heating Qheat,net,sec i,m depends on the parameter

γheat,sec i,m, which is the ratio between the heat gains due to radiation and internal heat

pro-duction Qg,heat,sec i,m and the heat losses due to transmission and ventilation QL,heat,sec i,m.

In case this ratio γheat,sec i,m is bigger or equal to 2.5, the monthly net energy need for

space heating is assumed to be zero. If γheat,sec i,m is smaller than 2.5, the monthly net

energy need for space heating equals the heat losses minus the product of the heat gains and an utilisation factor ηutil,heat,sec i,m, as shown below.

Qheat,net,sec i,m = QL,heat,sec i,m− ηutil,heat,sec i,m∗ Qg,heat,sec i,m (2.3.3)

The utilisation factor ηutil,heat,sec i,mdepends on the effective thermal capacity of the energy

sector and the heat transfer coefficients of transmission and ventilation. Both the heat gains and the heat losses are influenced by the outside temperature, the solar radiation, the possible sunblinds and the shading of the building.

Heat losses due to transmission and ventilation

The heat losses due to transmission (QT,heat, sec i,m) are calculated by multiplying the heat

transfer coefficient by the length of the month and the temperature difference between the monthly mean indoor temperature and the monthly mean outdoor temperature. The same is done for the calculation of the heat losses due to ventilation (QV,heat, sec i,m).

QL,heat,sec i,m = QT,heat, sec i,m+ QV,heat, sec i,m (2.3.4)

QT,heat, sec i,m= HT,heat,sec i∗ (18 − θe,m) ∗ tm (2.3.5)

QV,heat, sec i,m = HV,heat,sec i∗ (18 − θe,m) ∗ tm (2.3.6)

HT,heat,sec i and HV,heat,sec i are the heat transfer coefficients of respectively transmission

and ventilation. 18 is the imposed calculation value for the indoor temperature in the EPB calculation method. This means that it is assumed that the whole building unit has a constant indoor temperature of 18 °C. θe,m is the monthly mean outdoor temperature.

The current assumed values for each month are shown in Figure 2.2, together with the length of each month tm.

The monthly mean outdoor temperature will be influenced by climate change, resulting in lower heat losses if temperatures rise both for transmission and ventilation and therefore a lower monthly net energy need for space heating.

Heat gains due to radiation and internal heat production

The heat gains also consist of two terms, the monthly heat gains due to radiation and the monthly heat gains due to internal heat production. Only the heat gains due to radiation are influenced by the climate and will be discussed here. These heat gains Qs,heat,sec i,m

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Figure 2.2: Characteristic day, length, monthly mean outdoor temperature and mean total and diffuse radiation on a non shaded horizontal surface for each month

consist of three parts. The first part is the radiation through windows j, the second part is the radiation through non-ventilated passive sun energy systems k and the third is radiation due to adjacent unheated spaces l.

Qs,heat,sec i,m = m X j=1 Qs,heat,w,m,j+ n X k=1 Qs,heat,ps,m,k+ p X l=1 Qs,heat,sec i,U,m,l (2.3.7)

The radiation through windows is influenced by the solar radiation, the type of sunblind and the shading of the building and will be discussed in detail. The radiation through non-ventilated passive sun energy systems, which are for example walls with a window in front of them, and the radiation due to adjacent unheated spaces, are both influenced by the solar radiation. These influences are negligible because they are only present in a limited amount of buildings. For this reason, these systems will not be taken into account. The heat gains through window j during the observed month, Qs,heat,w,m,j, are calculated

as follows.

Qs,heat,w,m,j = 0.95 ∗ gm,j∗ Ag,j ∗ Is,m,j,shad (2.3.8)

gm,j = 0.9 ∗ (ac,mFc+ (1 − ac,m)) ∗ gg,L (2.3.9)

Is,m,j,shad= Is,dir,m,j,shad+ Is,dif,m,j,shad+ Is,refl,m,j,shad (2.3.10)

gm,j is the solar energy transmittance of window j and is determined based on the solar

energy transmittance of the transparent part gg,L and the type of sunshading. The factor

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or automatic. The reduction factor Fc takes the type of shading into account. For

sun-shadings that are parallel with the window, Fc depends on the location of the sunshading:

interior, exterior or in between the window glasses.

Ag,j is the glassed area of window j and Is,m,j,shad the radiation on a shadowed window

j. This radiation is the sum of the monthly direct (Is,dir,m,j,shad), diffuse (Is,dif,m,j,shad) and

reflected radiation (Is,refl,m,j,shad) on that window. These three are directly influenced by

the total and diffuse monthly radiation on a horizontal shadowed plane, Is,tot,m,hor and

Is,dif,m,hor. The currently used values for these radiations are shown in 2.2. Just as the

monthly mean temperatures, these will be influenced by the climate. In case of an increase in total and diffuse horizontal radiation, there will be a higher total radiation Is,m,j,shad

and therefore higher heat gains Qs,heat,w,m,j through windows.

The formulas for the direct, diffuse and reflected monthly radiation on a shadowed window are given in (2.3.11), (2.3.12) and (2.3.13). They do not only depend on the monthly radiation, but also on the shadowing of the window. This is implemented in the formula of the diffuse radiation by the parameter cn.

Is,dir,m,j,shad= (Is,tot,m,hor − Is,dif,m,hor) ∗

Qs,dir,char,j Qs,dir,char,hor (2.3.11) Is,dif,m,j,shad = Is,dif,m,hor ∗ ( 1 + cos(θj) 2 ) ∗ cm∗ cn (2.3.12) Is,refl,m,j,shad = 0.2 ∗ Is,tot,m,hor( 1 − cos(θj) 2 ) (2.3.13) The shadowing can either be by on obstacle in the environment of the building, for example the house of the neighbours, or by an overhang provided by the building itself. An obstacle is expressed by a horizon angle αh, which is the angle between the horizontal plane in

the middle of the window and the connection line between the middle of the window and the top of the obstacle. In absence of the real values, an angle of 25° is assumed for heat calculations and an angle of 15° for cooling and overheating. The overhang of the building itself is expressed by three angles, which are the angles between the plane of the window and the outer part of the overhang, either for an overhang at the left, the right or above the window. This results in respectively the angles αsL, αsR and αv. In absence of the real

values, these angles are assumed to be 0°. The four angles of the obstacle and overhangs determine the parameter cn together with the inclination of the window θj, and has an

influence on the monthly diffuse radiation.

Qs,dir,char,j and Qs,dir,char,hor in formulas (2.3.11), (2.3.12) and (2.3.13) are the daily direct

radiation on a shadowed plane j and on a shadowed horizontal plane for the characteristic day of the month. Both depend on the hour angle ω, the angle of incidence χ, the latitude ϕ and the characteristic day of each month. θj is the inclination of the window and cm

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2.3.2 Monthly primary energy need for space heating

In section 2.3.1, the monthly net energy need for space heating for one energy sector was determined in (2.3.3). This net energy is divided by the monthly mean system efficiency for space heating of sector i, ηsys,heat,sec i,m, to become the gross energy need for space

heating, as given in (2.3.14). This system efficiency is the ratio of the useful heat that the heat delivery system monthly delivers at the energy sector and the heat that the heat generation installation gives at the heat delivery system.

Qheat,gross,sec i,m =

Qheat,net,sec i,m

ηsys,heat,sec i,m

(2.3.14)

For the final energy need, the efficiency of the heat generation installations is also taken into account. Therefore, a distinction is made between the situation with one generation installation (preferred heat generator) and the situation with multiple installations in parallel (k non-preferred heat generators). The fractions fheat,m,pref and fheat,m,npref k in the

equations (2.3.15) and (2.3.16) give the fractions of the heat that are generated by respec-tively the preferred heat generator and the k non-preferred heat generators. fas,heat,sec i,m

is the part of the total energy need for space heating that is covered by the photovoltaic solar energy systems and ηgen,heat,pref and ηgen,heat,npref k the efficiencies of the generators.

Qheat,f inal,sec i,m,pref =

fheat,m,pref ∗ (1 − fas,heat,sec i,m) ∗ Qheat,gross,sec i,m

ηgen,heat,pref

(2.3.15)

Qheat,f inal,sec i,m,npref k =

fheat,m,npref k∗ (1 − fas,heat,sec i,m) ∗ Qheat,gross,sec i,m

ηgen,heat,npref k

(2.3.16) The monthly primary energy need of the EPW unit for space heating can be calculated by multiplying the final energy need by the conventional conversion factor fp,pref to primary

energy for the preferred generator or for the sum of the non-preferred generators for all the energy sectors i. This primary energy need for space heating is implemented in equation (2.3.2) to calculate the characteristic yearly primary energy use and the E-level.

Ep,heat,m =

X

i

(fp,pref∗ Qheat,final,sec i,m,pref+

X

k

(fp,npref k∗ Qheat,final,sec i,m,npref k)) (2.3.17)

2.3.3 Overheating and monthly net energy need for cooling

In Belgium, cooling is normally not needed in case the area of windows is not too big, there is exterior sunshading if needed, the floors and walls have a sufficient high thermal mass and the rooms are ventilated during the night. However, when cooling is needed, this is mostly roombound and depends on the desired temperature in the room, the ventilation possibilities, the internal heat gains and the heat gains through radiation. Nevertheless, the EPB calculation methods uses a simplified method, that determines the overheating possibility for each energy sector instead of for each room. The calculations are done in three steps.

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Overheating indicator

In the first step, the risk on overheating is estimated based on the normalised excessive heat gains, resulting in an overheating indicator Ioverh. If this overheating indicator is

higher than a maximum value of 6500 Kh (Kelvin hours), cooling will be needed and a cooling installation is installed. But in case the overheating indicator is not much lower than the maximum value, it can be that in the future, cooling is still needed, and an installation will be placed afterwards, resulting in a higher energy use. To act on this possibility that cooling may be needed after some time, although there is no cooling installation at the moment, the concept of fictitious cooling is introduced.

For the calculation of the overheating indicator, the annual normalised excessive heat gains are calculated, which are the sum over the months of the monthly normalised excessive heat gains. These are mainly determined by the monthly heat gains by radiation and internal heat production Qg,overh,sec i,m for overheating of energy sector i, which is equal to

the sum of the heat gains for radiation determined in (2.3.7) but adjusted for overheating and the monthly heat gains due to internal heat production. Besides that, they are also influenced by an utilisation factor ηutil,overh,sec i,m and the heat transfer coefficients

for transmission and ventilation HT,overh,sec i,m and HV,overh,sec i,m, that were determined in

(2.3.24) and (2.3.25). The formulas for the overheating indicator, the normalised excessive heat gains and the utilisation factor are given in (2.3.18), (2.3.19), (2.3.20) and (2.3.21).

Ioverh,sec i= Qexcess norm, sec i,a= 12

X

m=1

Qexcess norm,sec i,m (2.3.18)

Qexcess norm,sec i,m =

(1 − ηutil,overh,sec i,m) ∗ Qg,overh,sec i,m

HT,overh,sec i,m+ HV,overh,sec i,m

∗ 1000

3.6 (2.3.19)

ηutil,overh,sec i,m = am/(am+ 1) f or γoverh,sec i,m = 1 (2.3.20)

ηutil,overh,sec i,m =

1 − (γoverh,sec i,m)am

1 − (γoverh,sec i,m)am+1

f or all other cases (2.3.21) am is a parameter that depends on the effective thermal capacity of energy sector i and

the heat transfer coefficients for transmission and ventilation. The parameter γoverh,sec i,m

is the ratio of the monthly heat gains Qg,overh,sec i,m due to radiation and internal heat

production and the monthly heat losses QL,overh,sec i,mdue to transmission and ventilation,

calculated for overheating. The formulas for the heat losses are given below, and differ from those of the heat calculation by the assumption that overheating occurs from an indoor temperature above 23°C, which means that this is assumed as indoor temperature instead of 18 °C, and the additional term ∆θe,m, which is in increasing of the monthly

mean outdoor temperature with 1 °C. The heat transfer coefficient of ventilation for overheating differs from that for heating by an additional factor that takes the manual opening of the windows into account. This factor depends on the size of the energy sector

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and the potential of intensive ventilation.

γoverh,sec i,m = Qg,overh, sec i,m/QL,overh, sec i,m (2.3.22)

QL,overh,sec i,m = QT,overh, sec i,m+ QV,overh, sec i,m (2.3.23)

QT,overh, sec i,m= HT,overh,sec i,m ∗ (23 − (θe,m+ ∆θe,m)) ∗ tm (2.3.24)

QV,overh, sec i,m = HV,overh,sec i,m∗ (23 − (θe,m+ ∆θe,m)) ∗ tm (2.3.25)

Probability of cooling

In the second step, the conventional probability that active cooling will be needed after-wards is calculated. In case a cooling installation is present, the full cool load will be taken into account and the probability is 1, independent of the overheating indicator. In case no cooling installation is foreseen in the energy sector, a threshold value for the overheating indicator is used, equal to 1000 Kh. In case the overheating indicator is lower than this value, the probability that cooling will be needed in the future is 0. For values in between the threshold value of 1000 Kh and the maximum value of 6500 Kh of the overheating indicator, a linear probability for cooling between 0 and 1 will be used. This probability is given in (2.3.26).

pcool,sec i= max{0; min(

Ioverh,sec i− 1000

6500 − 1000 ; 1)} (2.3.26) Cooling

In the third step, the net energy need for cooling is calculated based on the excessive heat gains, taking the probability for cooling into account, as shown in (2.3.27). It is again assumed that cooling is needed for room temperatures higher than 23 °C. When active cooling is available, users will use the possibility of sunshading and ventilation less. Therefore, these two are taken into account in a different way for the calculation of the net energy need for cooling than for the risk on overheating in the first step. The principal net energy need for cooling Qcool,net,princ,sec i,m depends on the ratio λcool,sec i,m

between the monthly heat losses QL,cool,sec i,m and the monthly heat gains Qg,cool,sec i,m for

the energy sector, calculated for cooling. In case this ratio λcool,sec i,m is bigger or equal

to 2.5, Qcool,net,princ,sec i,m is zero. If λcool,sec i,m is smaller than 2.5, Qcool,net,princ,sec i,m is

determined as in (2.3.28).

Qcool,net,sec i,m = pcool,sec i∗ Qcool,net,princ,sec i,m (2.3.27)

Qcool,net,princ,sec i,m = Qg,cool,sec i,m− ηutil,cool,sec i,m∗ QL,cool,sec i,m (2.3.28)

The monthly heat gains due to radiation and internal heat production Qg,cool,sec i,m are

the same as Qg,overh,sec i,m but for cooling. The ratio λcool,sec i,m is the inverse of the

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(2.3.24) and (2.3.25), but with the subscript cool instead of overh. The utilisation factor ηutil,overh,sec i,m is the same as for overheating, but with λcool,sec i,m instead of γoverh,sec i,m in

equations (2.3.20) and (2.3.21).

2.3.4 Equivalent monthly primary energy need for cooling

As stated in paragraph 2.3.3, the EPB calculations take into account that there is a possibility that cooling will be needed in the future, although there is currently no cooling installation required. This results in an equivalent (fictitious) energy use for cooling that is calculated like there is a cooling installation available, for which the equivalent monthly electricity consumption is given in (2.3.29). The constant 8.1 is the product of the system efficiency 0.9, the energy-efficiency ratio EERtest of 2.5 of the (fictitious) cooling system

and the conversion factor for MJ to kWh of 3.6.

Qcool,f inal,sec i,m =

Qcool,net,sec i,m

8.1 (2.3.29)

With this equivalent monthly electricity consumption of one energy sector, the equivalent monthly primary energy need for cooling is calculated for the EPW unit in (2.3.30). fp is the conventional conversion factor to primary energy for electricity. This is the

primary energy need for cooling that is implemented in equation (2.3.2) to calculate the characteristic yearly primary energy use and the E-level.

Ep,cool,m =

X

i

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Chapter 3 Intergovernmental Panel on Climate

Change

3.1 Organisation

The Intergovernmental Panel on Climate Change (IPCC) is a body of the United Nations for evaluating the science related to climate change. The IPCC was created in 1988 by the World Meteorological Organization (WMO) and the United Nations Environment Programme (UNEP) and has currently 195 member countries. Its main goal is to provide governments and policymakers with information about climate change, the consequences and the potential future risks, as well as suggesting possible adaptations or mitigation options. The IPCC does not perform its research itself, but it unites and summarizes scientific papers about climate change and the possible consequences from all over the world guaranteeing objectivity and transparency [4].

The IPCC produces different types of reports. The Assessment Reports are the most important ones, which provide knowledge on climate change, the causes, the future risks and possible actions to reduce the impact. Besides the Assessment Reports, the IPCC also publishes Special Reports on a specific topic and Methodology Reports with guide-lines for the preparations of greenhouse gas inventories. The Synthesis Report combines the Assessment Report with Special Reports. The last Assessment Report is the Fifth Assessment Report, AR5, and dates from 2014. In this report, new climate scenarios were developed, the Representative Concentration Pathways (RCPs) [4] [5].

3.2 Representative Concentration Pathways

In the Fourth Assessment Report (AR4) of the IPCC in 2000, different climate scenarios were developed based on the greenhouse gas emissions, the Special Report on Emissions Scenarios (SRES). These scenarios are based on demographic, socio-economic, technolog-ical and social development during the 21st century. 40 storylines were developed that

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each describe a possible path. The four main scenarios are A1, A2, B1 and B2 with some sub-scenarios of A1. The scenario SRES A1 assumes for example a fast-growing economy, new or efficient technologies and a population peak around mid-century with a decline thereafter, as can be seen in Figure 3.1 where an overview is given of the different SRES scenarios. These scenarios assume that there is no adaptation in the behaviour of people during the climate changes. The possible emission reductions are due to environmental concerns that are taken into account, but not because of a changed behaviour of people. To take possible mitigation due to awareness or climate change experience into account, new scenarios were developed in 2014 in AR5, the Representative Concentration Pathways (RCPs) [6].

Figure 3.1: Overview SRES scenarios [6]

The RCP scenarios are based on chosen concentration pathways and their representative radiative forcing in 2100 and cover the whole range of published scenarios. This radiative forcing is among others based on the forcing of greenhouse gases. There are four scenar-ios, RCP8.5, RCP6, RCP4.5 and RCP2.6, for which the numbers represent the range of possible radiative forcing values in 2100 relative to pre-industrial values. RCP8.5 reaches for example a radiative forcing of +8.5 W/m² in 2100. The main difference with the previous SRES scenarios is that they do not start from emission and socio-economic sce-narios. The socio-economic scenarios are parallel developed with the climate projections. This has two big advantages, firstly the process is more efficient and faster, as the time-consuming climate projections can be developed simultaneously with the socio-economic and emission scenarios. This decoupling makes it possible to start faster with the climate modelling. Secondly, it is possible to have multiple emission levels for one socio-economic future outlook [7]. This means that the emission assumptions that resulted in the four RCPs are just an indication. It is possible that another combination of assumptions leads to the same radiative forcing, with as advantage that not all the emission scenarios have to be implemented in the climate models [8]. The sequential approach of the SRES sce-narios and the parallel approach of the RCP scesce-narios are shown in Figure 3.2. The full lines indicate transfers of information, the dashed lines stand for selection of RCPs and the dotted lines represent the integration of information and feedbacks [6].

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Figure 3.2: Approaches to the development of climate forcing scenarios [6]

The different approach of the SRES and the RCP scenarios results in scenarios with different greenhouse gas emissions and global temperature rises in time. The global CO2 equivalent concentration of both the SRES and the RCP scenarios is given in Figure 3.3. It can be seen that the RCP4.5 scenario equals the SRES B1 scenario after 2100, but has higher concentrations before 2100. RCP6.0 can be compared to the scenario A1B, with lower concentrations in the first centuries but higher concentrations in the end. The RCP8.5 scenario is even worse than the SRES A2 scenario.

Figure 3.3: Approaches to the development of climate forcing scenarios in time [9]

The RCP scenarios form the start for the research on the uncertainties of the climate models, the socio-economic scenarios and the future impact, but they do not provide a

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complete package for potential emissions, land-use or climate change in the 21st century, neither do they represent future policy decisions [9]. The RCPs not only differ from each other in the possible range of future radiative forces, but also in the amount of greenhouse gas emitted in the upcoming century. In RCP2.6, a peak in the annual greenhouse gas concentration is assumed between 2010-2020, with a decrease after 2020. In RCP4.5 and RCP6, the peaks are assumed respectively in 2040 and 2080 and decline thereafter. For RCP8.5, it is assumed that the emissions keep increasing until 2100. The pathways are ranges of greenhouse gas emissions rather than single values, as can be seen in Figure 3.4 [10], which illustrates the change in annual greenhouse gas emissions in time. An overview of the mean values and their likely range due to uncertainty and variability of the corresponding global mean surface temperature change and global mean sea level rise of the different RCP scenarios is given in Figure 3.5 for the reference periods 2046-2065 and 2081-2100.

Figure 3.4: Greenhouse gas emission pathways AR5 scenarios [10]