Heating and cooling energy demand and loads for building types in different countries of the EU

Transcription

1 Heating and cooling energy demand and loads for building types in different countries of the EU D2.3. of WP2 of the Entranze Project Written by: Paolo Zangheri, Roberto Armani, Marco Pietrobon and Lorenzo Pagliano (eerg) end-use Efficiency Research Group Politecnico di Milano Maria Fernandez Boneta (CENER) National Renewable Energy Centre Andreas Müller (EEG) Vienna University of Technology Reviewed by: Judit Kockat, Clemens Rohde Fraunhofer ISI March 214

2 ENTRANZE Project Year of implementation: April 212 September 214 Client: Web: EACI Project consortium: EEG NCRC Energy Economics Group Institute of Power Systems and Energy Economics Vienna University of Technology National Consumer Research Centre CENER Fraunhofer Society for the advancement of applied research National Renewable Energy Centre eerg Oeko end use Efficiency Research Group, Politecnico di Milano Öko-Institut SOFENA Sofia Energy Agency BPIE Buildings Performance Institute Europe Fraunhofer Enerdata Enerdata SEVEn SEVEn, The Energy Efficiency Center 2

3 The ENTRANZE project The objective of the ENTRANZE project is to actively support policy making by providing the required data, analysis and guidelines to achieve a fast and strong penetration of nzeb and RES-H/C within the existing national building stocks. The project intends to connect building experts from European research and academia to national decision makers and key stakeholders with a view to build ambitious, but reality proof, policies and roadmaps. The core part of the project is the dialogue with policy makers and experts and will focus on nine countries, covering >6% of the EU-27 building stock. Data, scenarios and recommendations will also be provided for EU-27 (+ Croatia and Serbia). This report provides an overview of the energy needs for heating, cooling and DHW 1 for several building types, located in different European climatic contexts. It includes main buildings characteristics of the base cases defined within WP2 2 and WP3 and first simulation results carried out during WP2. Acknowledgement: The authors and the whole project consortium gratefully acknowledge the financial and intellectual support of this work provided by the Intelligent Energy for Europe Programme. Legal Notice: The sole responsibility for the content of this publication lies with the authors. It does not necessarily reflect the opinion of the European Union. Neither the EACI nor the European Commission is responsible for any use that may be made of the information contained therein. All rights reserved; no part of this publication may be translated, reproduced, stored in a retrieval system, or transmitted in any form or by any means, electronic, mechanical, photocopying, recording or otherwise, without the written permission of the publisher. Many of the designations used by manufacturers and sellers to distinguish their products are claimed as trademarks. The quotation of those designations in whatever way does not imply the conclusion that the use of those designations is legal without the consent of the owner of the trademark. 1 DHW = domestic hot water 2 WP = work package 3

4 Content The ENTRANZE project... 3 Content... 4 Executive Summary Methodology Calculation tools EnergyPlus EN 1379 (simple hourly three-nodes model) INVERT/EE-Lab Thermal Module Reference building types Single house Apartment block Office School Reference climatic contexts Calculation of energy needs using the EnergyPlus tool Single house Seville (ES) Madrid (ES) Rome (IT) Milan (IT) Bucharest (RO) Vienna (AT) Paris (FR) Prague (CZ) Berlin (DE) Helsinki (FI) Apartment block Seville (ES) Madrid (ES) Rome (IT) Milan (IT) Bucharest (RO) Vienna (AT) Paris (FR) Prague (CZ) Berlin (DE)

5 2.2.1 Helsinki (FI) Office Seville (ES) Madrid (ES) Rome (IT) Milan (IT) Bucharest (RO) Vienna (AT) Paris (FR) Prague (CZ) Berlin (DE) Helsinki (FI) School Seville (ES) Madrid (ES) Rome (IT) Milan (IT) Bucharest (RO) Vienna (AT) Paris (FR) Prague (CZ) Berlin (DE) Helsinki (FI) Summary Comparison of the EnergyPlus results with the simply hourly EN1379 and INVERT/EE-Lab approach Single house Apartment block Office School Conclusion References

6 Executive Summary This ENTRANZE working paper presents the main results of the heating and cooling loads analysis and the energy demand for representative building types. The main contributors in this project are the end-use Efficiency Research Group of Politecnico di Milano (IT), the National Renewable Energy Centre (ES) and the Energy Economics Group from the Vienna University of Technology. Based on the definition of building types 3 and in partial overlap with the cost-optimality analysis 4, the results obtained within this Task (2.2) for several reference cases are used to calibrate the thermal calculation module in the model Invert/EE-Lab. With this model the thermal loads and energy needs (for heating, cooling and DHW) of all building types in investigated EU countries can be derived, which is the basis for the development of scenarios and policy analyses in work package 4. The calculation activities presented in this paper are carried out using a dynamic tool (EnergyPlus) adopting a common methodology of simulation. There are 4 different base cases (single family house, apartment block, office and school) in 1 relevant European cities with high relevance and characterised by different climatic conditions. The selected base cases referred to are national building stocks from 196 to 197. An online data tool prepared by the whole ENTRANZE consortium reflecting detailed thermal characteristics of building types. ENTRANZE is a European project that in principle covers all EU-27 countries. The geographical scope of the project is divided into target countries, focus countries and other countries. In terms of the scope, this deliverable (D2.3) focuses on the nine following countries: Austria, Bulgaria, Czech Republic, Finland, France, Germany, Italy, Romania and Spain. The report is structured as follows: Chapter 1 discusses the methodology used for estimating the thermal energy needs within the ENTRANZE Project. Furthermore, it defined the reference buildings and key climatic conditions. Chapter 2 shows the results obtained by the dynamic simulation. In Chapter 3, the results using a simplified tool (spreadsheet) based on the Standard EN ISO 1379 and the results from the dynamic simulation are compared with the outcomes of the thermal module in Invert/EE-Lab. Finally, the differences of the described approaches are concluded and discussed. 3 For results on the building stock analysis conducted in work package 2, please refer to the online data tool country reports or the country reports 4 For results on the analysis of global cost performed in work package 3, please refer to the following report: 6

7 1. Methodology In order to achieve a higher quality of outputs, the estimation of energy need is carried out in close connection with the previous data collection phase (Task 2.1-3) as well as with the tools applied in WP3 and WP4. Task data collection selection ES CZ FR FI AU BU IT DE WP3 Task 2.2 simulation-calculation EN1379 INVERT calibration WP4 calculation INVERT comparison Fig. 1: Entranze activities involved in the calculation of the energy needs. In the following paragraphs we discuss the tools used, as well as the preliminary results obtained, about the characterisation of building prototypes and the selection of key climatic conditions within the Entranze Target area. 1.1 Calculation tools EnergyPlus EnergyPlus is one of the most used and reliable program for energy analysis and thermal load simulation. It is based on a description of the building s physical make-up, its associated mechanical systems, etc. EnergyPlus is able to calculate the heating and cooling loads dynamically. The loads are necessary to maintain thermal control setpoints and conditions. Moreover, the tool covers secondary HVAC system, coil loads, the energy 7

8 consumption of primary plant equipment and other simulation details. These data are necessary to verify that the simulation is performing as the actual building would. Many of the simulation characteristics have been inherited from the legacy programs of BLAST and DOE 2. Fig. 2: EnergyPlus Program Schematic. The principal program modules involved in the calculation of the energy need for heating and cooling are the Surface Heat Balance and Air Heat Balance managers (with their sub-modules). For more information see EnergyPlus Engineering Reference EN 1379 (simple hourly three-nodes model) This method takes in account the calculation for: thermal exchange for transmission and ventilation of the building zone when it is warmed or cooled at a constant set point temperature, the contribution of internal and solar thermal gain and, as a consequence, the annual energy need for heating and cooling of thermal zones. The used procedure calculates only the sensible component of heating and cooling (whereas the latent component is not considered). The building is described as a single zone with boundaries are determined by surfaces in contact with external air, ground or non-conditioned zones

9 The considered geometrical input data has been: - the total net floor surface and the net volume of the inner air of the thermal zone (building); - the areas of building surfaces (both opaque and transparent) that delimitates the thermal zone, subdivided on the base of exposition. Only surfaces facing on external air, ground or non-conditioned zones were entered; - the percentage of transparent surfaces on total areas (both opaque and transparent) for each exposition; - the length of thermal bridges. The required physical proprieties have been: - the average thermal transmittance and absorption coefficient, for opaque surfaces; - the average thermal transmittance and the their solar factor, for windows; - the linear thermal transmittance, for thermal bridges. The internal thermal capacity of the zone can be determined according to EN ISO standard, or by adopting default values indicated in EN 1379 standard on the base of qualitative evaluation of the mass of building components [J/K] (very light, light, medium, heavy, very heavy). With the aim to simplify the data collection (according to Invert Model requirements), the default values reported in EN 1379 standard are applied to the net floor surfaces only. The simplified hourly dynamic method for energy need calculation allows the definition of the time schedule for monthly and hourly functioning of different building parameters. In the former case, the monthly schedule data which can be entered are: heating and cooling periods, solar shading activation and free cooling strategies by means of night ventilation (between 11 p.m. to 7 a.m. for all days of the cooling period). In the latter case, the inputted hourly schedule data are: internal gain [W/m 2 ], air exchanges [h -1 ] for both heating and cooling periods, the extra air flow due to night ventilation [h -1 ] only in the cooling period as well as the set point temperatures for both heating and cooling. However, it is not possible to insert weekly schedule considering a different building use during week end. The required climate data have been: - average monthly temperature [ C]; - average of daily max temperature for each month [ C]; - monthly mean daily solar radiation on horizontal surfaces [kj/(m 2 day)]; - latitude; The sensible thermal energy need for heating and cooling are calculated starting from: - thermal exchange for transmission between conditioned zone and outdoor, that is depending of the temperature difference between these zones; 9

10 - thermal exchange for ventilation (natural or mechanical ventilation), that is depending of the difference between indoor temperature and temperature of supplied air; - internal thermal gain due to people presence, lighting, appliances and due to the heating loss by heating, cooling, hot water and ventilation plants; - solar thermal gain (from windows and opaque surfaces); - heat storage or loss by building mass. The used method is based on the hourly R-C methods at three nodes (resistive capacitive equivalent method hourly at three nodes). This model determines a distinction between indoor air temperature and mean radiant surface temperatures (for internal surfaces facing an zone to be calculated). This approach increases the accuracy by taking into account the radiant and convective components of thermal, solar and lighting internal gain. The energy need for heating and cooling is determined by calculating the monthly mean power for heating and cooling ( HC,nd positive for heating and negative for cooling) required (through extraction or supply of air power) for each hour by corresponding node of internal air, air, to maintain a min or max determined temperature. The set point temperature is a weighted average between air temperature and mean radiant temperature. The default weighted factor for both temperature is,5. The thermal exchange for ventilation - H ve - is directly related to the node that corresponds to air temperature - air - and to the node which represents the supplied air temperature - sup. The thermal exchange for transmission is separated in the windowed portion (including doors and windows) - H tr,w -, which is considered as thermal mass=. The remaining portion of the surfaces - H tr,op -, includes a thermal mass, which is, in turn, split in two part: H tr,em (emission) and H tr,ms (conductance). The solar and indoor thermal gain are distributed on node of air, air, on central node, s (which is a combination of air and mean radiant temperature - r,mn ) and on the node representing the mass of the building zone, m. The thermal mass is defined by a single thermal capacity, C m, localized between H tr,ms and H tr,em. A coupling conductance is defined between node of internal air and central node. The thermal flow due to inner heat sources, int, and the thermal flow due to solar thermal sources, sol, are subdivided between the three nodes. 1

11 Fig. 3: Scheme of the R-C methods at three nodes INVERT/EE-Lab Thermal Module Invert/EE-Lab is a bottom-up simulation tool that evaluates the effects of different promotion schemes (in particular different settings of economic and regulatory incentives) on the energy carrier mix, CO 2 reductions and costs for RES-H support policies. Furthermore, Invert/EE-Lab is designed to simulate different scenarios (price scenarios, insulation scenarios, different consumer behaviours, etc.) and their respective impact on future trends of renewable as well as conventional energy sources on a national and regional level. The core of the tool is a myopical, nested logit approach, which optimizes objectives of agents under imperfect information conditions and by that represents the decisions maker concerning building related decisions. Invert/EE-Lab models the stock of buildings in a highly disaggregated manner. Therefore the simulation tool reflects some characteristics of an agent based simulation. A main output of the model Invert/EE-Lab is the annual delivered energy for each building segment. The delivered energy is derived from the monthly energy need (energy need for space heating, cooling and hot water). This document deals only with the calculation of the energy need, not with the other modules of the model Invert/EE-Lab. 11

12 The overall energy need of the building stock in a region or country is modelled for monthly based on the energy need in each building class (subset of buildings which share the same building geometry and envelope specifications, climate zone and usage). The energy need of each building class is calculated by using a monthly energy balance approach, quasi-steady-method, enhanced by explicitly distinguishing between using and non-using days and in case for ventilation between average day (16 hours) and night (8 hours) outside air temperatures. The methodology has been implemented based on the following literature, Austrian, German and EU standards: 1. Pöhn Christian: "Bauphysik. Erweiterung 1, Energieeinsparung und Wärmeschutz ; Energieausweis - Gesamtenergieeffizienz, Vienna: Springer-Verlag/Wien, 27; 2. ÖNORM B 811-5; 3. ÖNORM H 855; 4. DIN V 418-6; 5. DIN V 471-1; 6. EN ISO 1379: 28: Quasi-steady-state method. The monthly energy need for space heating is calculated as a balance of heat gains and heat transfers and is multiplied by the utilization factor (see equation 1). The monthly utilization factor shows that only a part of the internal and solar heat gains are utilized to decrease the energy need for heating. The utilization factor depends (mostly) on the building construction type. The annual energy need is the sum of monthly energy needs. (1) where: is the total heat transfer [MJ]; is the monthly utilization factor; are the total heat gains [MJ]. The monthly heat transfer is the sum of the transfer by transmission and transfer by ventilation. The calculation of the month heat transfer requires the set-point temperature of the building, which is determined for each user profile and the monthly temperature of the external environment. To calculate the heat transfer by transmission, the data of the building elements, which are the area and the thermal transmittance of a building element, are taken into account. To calculate the heat transfer by ventilation, the buildings are split into buildings with ventilation system and without ventilation system. If there are no ventilation system losses by transmission losses are equal to 12

13 window ventilation. In another case the transfer by ventilation depends on the heat recovery unit of a ventilation appliance. The monthly heat gains are calculated as a sum of the monthly internal heat gains and monthly solar heat gains. Calculation of the internal heat gains require the specific heat gain, which is taken for each user profile, and net floor area of the building. Solar heat gains from solar heat sources result from solar radiation through the windows with consideration of shading reduction factors. These require establishing the glass area of a building, solar energy transmittance of glazing and the shading corrector factor. The monthly energy need for space cooling is calculated equal to the energy for space heating, but this heat has to be extracted from a conditioned space. However, additional to the calculation of the solar heat gains the shading factor and solar heat input through opaque building elements are considered. The shading factor depends on the material type of the shading element. In order to calculate heat input through opaque building elements, correcting factor regarding the colour and angle of the relevant building components are used. The monthly energy need for hot water is calculated by introducing specific energy need for warm water which is defined for each user profile and is given in table user profiles. It is calculated as shown in equation 2:, 1 1 where: wwwb is the specific energy need for warm water; BF is the gross floor area [m 2 ]; are the days per month [d/m]. (2) 1.2 Reference building types Evaluating as strategic the building renovation sector in accordance with the premises and objectives of the Entranze Project this analysis has been focused on the building age more representative of the existing building stock in EU: years 6 s - 7 s. Starting from the data collected within Task 2.1 and through additional data collection templates, we characterized 4 building types (2 residential and 2 tertiary) in 8 target countries. Due to similarities of the climate between Romania and Bulgaria, the latter was not simulated in this report, although it is target country in other tasks of the project ENTRANZE. 13

14 1.2.1 Single house The building which has been selected as reference for single family house, (composed by an underground level and two floors over ground level) has a conditioned surface of about 14 m 2 and a S/V ratio of.7. The main other characteristics (fixed and variable by Country) involved on the simulation task are shown below. North façade South façade East façade West façade Fig. 4: Prospects of the single house model. 14

15 Tab. 1: Fixed characteristics of the single house model. All Countries N of heated floor = 2 S/V ratio =.7 m 2 /m 3 Orientation: S/N Net dimensions of heated volume = 8.5 x 8.5 x 6 m Net floor area of heated zones = 14 m 2 Area of S façade = 51 m 2 Area of E façade = 51 m 2 Area of N façade = 51 m 2 Area of W façade = 51 m 2 Area of Roof = 72. m2 Area of Basement = 72. m2 Window area on S façade = % Window area on E façade = 7% Window area on N façade = % Window area on W façade = 7% People design level = 5 m 2 /people Lighting design level = 3.5 W/m2 Appliances design level = 4 W/m 2 Building technologies Building geometry Internal gains Tab. 2: Variable characteristics of the single house model. ES IT RO AT FR CZ DE FI Construction materials: A A A A A A A B Typical infiltration rate: h 1 U value of wall = W/m 2 K U value of roof = W/m 2 K U value of basement = W/m 2 K U value of glass = W/m 2 K g value of glass = Passive In Summer: shading device + ventilation at strategies: night A: Brick, concrete, plaster B: Brick, insulation, concrete, plaster

16 1.2.2 Apartment block The building which has been selected as reference for apartment block (four floors + cellar) is composed by about flats and its conditioned area is around 1 m 2 and a S/V ratio of.33. The main other characteristics (fixed and variable by Country) involved on the simulation task are shown below. North façade South façade Fig. 5: Prospects of the apartment block model. 16

17 Tab. 3: Fixed characteristics of the apartment block model. ES, IT, FR Building geometry Internal gains N of heated floor = 4 S/V ratio =.33 m 2 /m 3 Orientation: S/N RO, AT, CZ, DE, FI Net dimensions of heated volume = 24.6 x 11.2 x 12.8 m Net floor area of heated zones = 99 m 2 Area of S façade = 3 m 2 Area of E façade = 143 m 2 Area of N façade = 3 m 2 Area of W façade = 143 m 2 Area of Roof = 54 m 2 Area of Basement = 54 m2 Window area on S façade = % 3% Window area on E façade = % % Window area on N façade = % 3% Window area on W façade = % % People design level = m 2 /people Lighting design level = 3.5 W/m 2 Appliances design level = 4 W/m 2 Tab. 4: Variable characteristics of the apartment block model. ES IT RO AT FR CZ DE FI Building technologies Construction materials: Typical ACH rate: U value of wall = U value of roof = U value of basement = U value of glass = g value of glass = Passive strategies: A A C B B B B B h W/m 2 K W/m 2 K W/m 2 K W/m 2 K A: Hollow brick, air gap, concrete, plaster B: Concrete, plaster C: Prefabricated panel, concrete, plaster In Summer: shading device + ventilation at night 17

18 1.2.3 Office As reference of office building, a medium-size and highly-glazed office building has been selected, with 5 floors (of 3 m height each) an S/V ratio of,33 and a net heated area of 24 m 2. The main other characteristics (fixed and variable by Country) involved on the simulation task are shown in the following tables. South façade East façade North façade West façade Fig. 6: Prospects of the office building model. 18

19 Tab. 5: Fixed characteristics of the office building model. All Countries N of heated floor = 5 S/V ratio =.33 m 2 /m 3 Orientation: S/N Net dimensions of heated volume = 3 x 16 x m Net floor area of heated zones = 24 m 2 Area of S façade = 45 m 2 Area of E façade = 24 m 2 Area of N façade = 45 m 2 Area of W façade = 24 m 2 Area of Roof = 48 m2 Area of Basement = 48 m2 Window area on S façade = 56% Window area on E façade = 32% Window area on N façade = 5% Window area on W façade = 35% People design level = 18 m 2 /people Lighting design level = 14 W/m2 Appliances design level = 9 W/m 2 Building technologies Building geometry Internal gains Tab. 6: Variable characteristics of the office building model. ES IT RO AT FR CZ DE FI Construction materials: A A C B A C B B Typical ACH rate: h 1 U value of wall = W/m 2 K U value of roof = W/m 2 K U value of basement = W/m 2 K U value of glass = g value of glass = Passive strategies: A: Hollow brick, air gap, concrete, plaster B: Concrete, insulation, plaster C: Prefabricated panel, concrete, plaster W/m 2 K Shading device controlled in summer by occupant 19

20 1.2.4 School As reference model of school a two-floors building has been selected. It is U shaped with a heated surface of 35 m 2 and its S/V ratio is.46. The main other characteristics (fixed and variable by Country) involved on the simulation task are shown in the following tables. South/North façade East façade West façade Fig. 7: Prospects of the school model. 2

21 Tab. 7: Fixed characteristics of the school model. All Countries N of heated floor = 2 S/V ratio =.46 m2/m3 Orientation: S/N Net dimensions of heated volume = 45 x 6 x 7 m (U shape) Net floor area of heated zones = 35 m 2 Area of S façade = m 2 Area of E façade = 3 m 2 Area of N façade = m 2 Area of W façade = 3 m 2 Area of Roof = 175 m2 Area of Basement = 175 m2 Window area on S façade = 32% Window area on E façade = 22% Window area on N façade = 29% Window area on W façade = 4% People design level = 5.6 m 2 /people Lighting design level = 12 W/m2 Appliances design level = 1.75 W/m 2 Building geometry Internal gains Tab. 8: Variable characteristics of the school model. ES IT RO AT FR CZ DE FI Construction materials: A A C B A B B A Typical ACH rate: h 1 U value of wall = W/m 2 K U value of roof = W/m 2 K U value of basement = W/m 2 K U value of glass = W/m 2 K g value of glass = Passive strategies: In Summer: shading device + ventilation at night Building technologies A: Hollow brick, air gap or insulation, concrete, plaster B: Concrete, insulation, plaster C: Prefabricated panel, concrete, plaster 21

22 1.3 Reference climatic contexts In order to calculate, with stationary, quasi-stationary or dynamic calculation methods the energy and comfort performances of buildings, climatic conditions are usually represented as sets of data that describe at different degrees of detail, an average climate at a certain location. Since there are a number of weather variables that affect building behaviour it is not straightforward to establish a definition of average weather. Different definitions and hence different types of data sets are available (IWEC, TRY, TMY, Meteonorm, etc.) based on different weighting of the parameters and other choices. Weather data sets can be in the form of a year of hourly data (8 76 hours) synthesized to represent long-term statistical trends and patterns and are used for hourly calculations of energy and power demand of a building. As performing building simulations for every Entranze Target Country would have been be time consuming (compared to our resources) it has been decided to select 1 key climatic conditions within this European area. As reference indicators have been used the Winter Severity Index and the Summer Severity Index proposed by F. Sanchez de la Florthe (), as well the Climatic Cooling Potential by Artmann (27). We calculated this indexes for cities, within the Entranze Target Countries, selected in function of the nominal climatic characteristics, the availability of homogeneous climatic data (IWEC 6 ) and their relevance (in terms of urban population). 6 The International Weather for Energy Calculations (IWEC) are the result of ASHRAE Research Project 1 by Numerical Logics and Bodycote Materials Testing Canada for ASHRAE Technical Committee 4.2 Weather Information. The IWEC data files are 'typical' weather files suitable for use with building energy simulation programs, derived from up to 18 years of DATSAV3 hourly weather data. 22

23 3. Summer Severity Index versus Winter Severity Index for European cities (within the Entranze Target Countries) SCS WCS Helsinki Munich Prague Innsbruck Vien Bucharest Sofia Berlin Milan Costanta Dusseldorf Lyon Varna Paris Madrid Bordeaux Marseille Foggia Rome Barcelona Genova Bilbao La Coruna Sevilla Palermo Fig. 8: SCS versus WCS for European cities. 3. Summer Severity Index versus Climatic Cooling Potential (in July) for European cities (within the Entranze Target Countries) SCS CCP Helsinki Munich Prague Innsbruck Vien Bucharest Sofia Berlin Milan Costanta Dusseldorf Lyon Varna Paris Madrid Bordeaux Marseille Foggia Rome Barcelona Genova Bilbao La Coruna Sevilla Palermo Fig. 9: SCS versus CCP for European cities. 23

24 Analysing the data obtained we selected as key climatic conditions those of: Seville (ES), Madrid (ES), Rome (IT), Milan (IT), Bucharest (RO), Vienna (AT), Paris (FR), Prague (CZ), Berlin (DE), Helsinki (FI). Tab. 9: Characterisation of the 1 selected climates. Context Climatic characterisation Relevance Seville (ES) Mediterranean climate (hot summer subtype) with very low climatic cooling potential (extreme summer Medium conditions) Madrid (ES) Semi-arid climate with low climatic cooling potential High Rome (IT) Mediterranean climate (warm summer subtype) with medium climatic cooling potential High Milan (IT) Humid subtropical climate with medium climatic cooling potential High Bucharest (RO) Vienna (AT) Paris (FR) Prague (CZ) Berlin (DE) Helsinki (FI) Humid continental (hot summer subtype) / Subarctic climate with medium climatic cooling potential Humid continental climate (warm summer subtype) with high climatic cooling potential Oceanic climate with very high climatic cooling potential Humid continental climate (warm summer subtype) with high climatic cooling potential Humid continental climate (warm summer subtype) with high climatic cooling potential Humid continental / Subarctic climate (extreme winter conditions) High High High High High Medium 24

25 Fig. 1: Key and secondary weather conditions selected for the Task2.2 and WP3 activities.

26 2. Calculation of energy needs using the EnergyPlus tool The simulation/calculation campaign has been carried out within the EnergyPlus dynamic simulation environment (version ) and applying the Standard EN for estimating the DHW demand. For obtaining building envelopes fully comparable in terms of comfort performance (indoor comfort), the energy need for all the building variants are calculated assuming the same indoor conditions for each typology: - the same operative temperature and relative humidity setpoints: o residential buildings: 2 C in winter and 26 C in summer (latent control not applied); o tertiary buildings: 21 C and 3% in winter; 26 C and 7% in summer. - the same values of minimum air change (at maximum occupation rate), coherent with the assumed occupation levels and the ventilation rates proposed by EN1 for very low-polluted buildings: o.5 h -1 in the residential buildings; o.8 h -1 in the office building; o 1.6 h -1 in the school. The results obtained are shown in the following paragraphs in terms of monthly [kwh/m 2 ] and hourly [Wh/m 2 ] energy need for heating, cooling and DHW. 26

27 2.1 Single house Seville (ES) Figure 11: Monthly energy needs for heating, cooling and DHW of the single house located in Seville. Figure 12: Hourly energy needs for heating and cooling of the single house located in Seville. 27

28 2.1.2 Madrid (ES) Figure 13: Monthly energy needs for heating, cooling and DHW of the single house located in Madrid. Figure 14: Hourly energy needs for heating and cooling of the single house located in Madrid. 28

29 2.1.3 Rome (IT) Figure : Monthly energy needs for heating, cooling and DHW of the single house located in Rome. Figure 16: Hourly energy needs for heating and cooling of the single house located in Rome. 29

30 2.1.4 Milan (IT) Figure 17: Monthly energy needs for heating, cooling and DHW of the single house located in Milan. Figure 18: Hourly energy needs for heating and cooling of the single house located in Milan. 3

31 2.1.5 Bucharest (RO) Figure 19: Monthly energy needs for heating, cooling and DHW of the single house located in Bucharest. Figure 2: Hourly energy needs for heating and cooling of the single house located in Bucharest. 31

32 2.1.6 Vienna (AT) Figure 21: Monthly energy needs for heating, cooling and DHW of the single house located in Vienna. Figure 22: Hourly energy needs for heating and cooling of the single house located in Vienna. 32

33 2.1.7 Paris (FR) Figure 23: Monthly energy needs for heating, cooling and DHW of the single house located in Paris. Figure 24: Hourly energy needs for heating and cooling of the single house located in Paris 33

34 2.1.8 Prague (CZ) Figure : Monthly energy needs for heating, cooling and DHW of the single house located in Prague. Figure 26: Hourly energy needs for heating and cooling of the single house located in Prague. 34

35 2.1.9 Berlin (DE) Figure 27: Monthly energy needs for heating, cooling and DHW of the single house located in Berlin. Figure 28: Hourly energy needs for heating and cooling of the single house located in Berlin. 35

36 2.1.1 Helsinki (FI) Figure 29: Monthly energy needs for heating, cooling and DHW of the single house located in Helsinki. Figure 3: Hourly energy needs for heating and cooling of the single house located in Helsinki. 36

37 2.2 Apartment block Seville (ES) Figure 31: Monthly energy needs for heating, cooling and DHW of the apartment block located in Seville. Figure 32: Monthly energy needs for heating, cooling and DHW of the apartment block located in Seville 37

38 2.2.2 Madrid (ES) Figure 33: Monthly energy needs for heating, cooling and DHW of the apartment block located in Madrid. Figure 34: Monthly energy needs for heating, cooling and DHW of the apartment block located in Madrid. 38

39 2.2.3 Rome (IT) Figure 35: Monthly energy needs for heating, cooling and DHW of the apartment block located in Rome. Figure 36: Monthly energy needs for heating, cooling and DHW of the apartment block located in Rome. 39

40 2.2.4 Milan (IT) Figure 37: Monthly energy needs for heating, cooling and DHW of the apartment block located in Milan. Figure 38: Monthly energy needs for heating, cooling and DHW of the apartment block located in Milan. 4

41 2.2.5 Bucharest (RO) Figure 39: Monthly energy needs for heating, cooling and DHW of the apartment block located in Bucharest. Figure 4: Monthly energy needs for heating, cooling and DHW of the apartment block located in Bucharest. 41

42 2.2.6 Vienna (AT) Figure 41: Monthly energy needs for heating, cooling and DHW of the apartment block located in Vienna. Figure 42: Monthly energy needs for heating, cooling and DHW of the apartment block located in Vienna. 42

43 2.2.7 Paris (FR) Figure 43: Monthly energy needs for heating, cooling and DHW of the apartment block located in Paris. Figure 44: Monthly energy needs for heating, cooling and DHW of the apartment block located in Paris. 43

44 2.2.8 Prague (CZ) Figure 45: Monthly energy needs for heating, cooling and DHW of the apartment block located in Prague. Figure 46: Monthly energy needs for heating, cooling and DHW of the apartment block located in Prague. 44

45 2.2.9 Berlin (DE) Figure 47: Monthly energy needs for heating, cooling and DHW of the apartment block located in Berlin. Figure 48: Monthly energy needs for heating, cooling and DHW of the apartment block located in Berlin. 45

46 2.2.1 Helsinki (FI) Figure 49: Monthly energy needs for heating, cooling and DHW of the apartment block located in Helsinki. Figure 5: Monthly energy needs for heating, cooling and DHW of the apartment block located in Helsinki. 46

47 2.3 Office Seville (ES) Figure 51: Monthly energy needs for heating, cooling and DHW of the office building located in Seville. Figure 52: Hourly energy needs for heating and cooling of the office building located in Seville. 47

48 2.3.2 Madrid (ES) Figure 53: Monthly energy needs for heating, cooling and DHW of the office building located in Madrid. Figure 54: Hourly energy needs for heating and cooling of the office building located in Madrid. 48

49 2.3.3 Rome (IT) Figure 55: Monthly energy needs for heating, cooling and DHW of the office building located in Rome. Figure 56: Hourly energy needs for heating and cooling of the office building located in Rome. 49

50 2.3.4 Milan (IT) Figure 57: Monthly energy needs for heating, cooling and DHW of the office building located in Milan. Figure 58: Hourly energy needs for heating and cooling of the office building located in Milan. 5

51 2.3.5 Bucharest (RO) Figure 59: Monthly energy needs for heating, cooling and DHW of the office building located in Bucharest. Figure 6: Hourly energy needs for heating and cooling of the office building located in Bucharest. 51

52 2.3.6 Vienna (AT) Figure 61: Monthly energy needs for heating, cooling and DHW of the office building located in Vienna. Figure 62: Hourly energy needs for heating and cooling of the office building located in Vienna. 52

53 2.3.7 Paris (FR) Figure 63: Monthly energy needs for heating, cooling and DHW of the office building located in Paris. Figure 64: Hourly energy needs for heating and cooling of the office building located in Paris. 53

54 2.3.8 Prague (CZ) Figure 65: Monthly energy needs for heating, cooling and DHW of the office building located in Prague. Figure 66: Hourly energy needs for heating and cooling of the office building located in Prague. 54

55 2.3.9 Berlin (DE) Figure 67: Monthly energy needs for heating, cooling and DHW of the office building located in Berlin. Figure 68: Hourly energy needs for heating and cooling of the office building located in Berlin. 55

56 2.3.1 Helsinki (FI) Figure 69: Monthly energy needs for heating, cooling and DHW of the office building located in Helsinki. Figure 7: Hourly energy needs for heating and cooling of the office building located in Helsinki. 56

57 2.4 School Seville (ES) Figure 71: Monthly energy needs for heating, cooling and DHW of the school located in Seville. Figure 72: Hourly energy needs for heating and cooling of the school located in Seville. 57

58 2.4.2 Madrid (ES) Figure 73: Monthly energy needs for heating, cooling and DHW of the school located in Madrid. Figure 74: Hourly energy needs for heating and cooling of the school located in Madrid. 58

59 2.4.3 Rome (IT) Figure 75: Monthly energy needs for heating, cooling and DHW of the school located in Rome. Figure 76: Hourly energy needs for heating and cooling of the school located in Rome. 59

60 2.4.4 Milan (IT) Figure 77: Monthly energy needs for heating, cooling and DHW of the school located in Milan. Figure 78: Hourly energy needs for heating and cooling of the school located in Milan. 6

61 2.4.5 Bucharest (RO) Figure 79: Monthly energy needs for heating, cooling and DHW of the school located in Bucharest. Figure 8: Hourly energy needs for heating and cooling of the school located in Bucharest. 61

62 2.4.6 Vienna (AT) Figure 81: Monthly energy needs for heating, cooling and DHW of the school located in Vienna. Figure 82: Hourly energy needs for heating and cooling of the school located in Vienna. 62

63 2.4.7 Paris (FR) Figure 83: Monthly energy needs for heating, cooling and DHW of the school located in Paris. Figure 84: Hourly energy needs for heating and cooling of the school located in Paris 63

64 2.4.8 Prague (CZ) Figure 85: Monthly energy needs for heating, cooling and DHW of the school located in Prague. Figure 86: Hourly energy needs for heating and cooling of the school located in Prague 64

65 2.4.9 Berlin (DE) Figure 87: Monthly energy needs for heating, cooling and DHW of the school located in Berlin. Figure 88: Hourly energy needs for heating and cooling of the school located in Berlin. 65

66 2.4.1 Helsinki (FI) Figure 89: Monthly energy needs for heating, cooling and DHW of the school located in Helsinki. Figure 9: Hourly energy needs for heating and cooling of the school located in Helsinki. 66

67 2.5 Summary Table 1: Summary of simulated energy needs for heating, cooling and DHW for the single house base cases. Target Reference Country weather end-use Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual TOTAL EN Heating 11,2 6,2 2,6 1,5,1,,,,,1 5,2 9,8 36,7 ES Seville Cooling,,,,, 14,,4 2,7 12,8,,, 72,9 123,7 kwh/m 2 DHW 1,2 1,1 1,2 1,2 1,2 1,2 1,2 1,2 1,2 1,2 1,2 1,2 14,1 Heating,9 18,2 8,5 6,2,6,,,, 3,9 14, 26,6 13,9 ES Madrid Cooling,,,,, 8,2 19,2,6 4,7,,, 47,7 166,4 kwh/m 2 DHW 1,3 1,1 1,3 1,2 1,3 1,2 1,3 1,3 1,2 1,3 1,2 1,3 14,8 Heating 18,8 11,7 7,3 2,1,3,,,, 2,8 7,5 16,7 67,1 IT Rome Cooling,,,,, 8, 16,5,2 6,,,, 45,8 127,7 kwh/m 2 DHW 1,3 1,1 1,3 1,2 1,3 1,2 1,3 1,3 1,2 1,3 1,2 1,3 14,8 Heating 39,9 3,1 14,2 8, 1,1,,,, 7,2 23,4 37, 16,9 IT Milan Cooling,,,,, 7,4 14,4 8,7 1,8,,, 32,4 28,9 kwh/m 2 DHW 1,3 1,2 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3,6 Heating 45,5 3,6 21,1 6,6 1,5,,, 1,5 11,7 28,5 42,1 189,1 RO Bucharest Cooling,,,,, 7,9 13, 1,1,,,, 31, 236, kwh/m 2 DHW 1,3 1,2 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3,9 Heating 43,4 35,5 21,3 1, 2,1,3,, 1,9 12,3 29,2 43,5 199,5 AT Vienna Cooling,,,,, 1,2 7,1 6,2,,,, 14,5 229,9 kwh/m 2 DHW 1,3 1,2 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3,9 Heating 35,1 29,5 21,8 11,6 3,2,5,, 2,5 11,6,9 34,2 176, FR Paris Cooling,,,,,, 4,3 3,1,,,, 7,4 199,3 kwh/m 2 DHW 1,3 1,2 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3,9 Heating 48,5 4,1 28, 14,9 5,3,,, 5,2 18,5 35,9 43, 239,4 CZ Prague Cooling,,,,, 1,2 2,1 1,8,,,, 5,2 26,5 kwh/m 2 DHW 1,3 1,2 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3,9 Heating 33,4 3, 21,2 9,5 3,2,,, 2,2 11,4 24,9 32,9 168,7 DE Berlin Cooling,,,,, 3,1 3,5 2,2,,,, 8,9 193,5 kwh/m 2 DHW 1,3 1,2 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3 1,3,9 Heating 31,2 26,9 2,9 9,8 1,6,,, 4,2 13,3 26,4 31, 165,2 FI Helsinki Cooling,,,,,,, 1,1,,,, 1,1 183,1 kwh/m 2 DHW 1,4 1,3 1,4 1,4 1,4 1,4 1,4 1,4 1,4 1,4 1,4 1,4 16,8 Single House 67

68 Table 11: Summary of simulated energy needs for heating, cooling and DHW for the apartment block base cases. Target Reference Country weather end-use Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual TOTAL EN Heating 6,8 3,9 1,8 1,1,2,,,,, 3, 5,7 22,5 ES Seville Cooling,,,,, 6,6 14,5 11,5 7,1,,1, 39,8 83,9 kwh/m 2 DHW 1,8 1,7 1,8 1,8 1,8 1,8 1,8 1,8 1,8 1,8 1,8 1,8 21,6 Heating,9 11,3 6,1 4,7,3,,,, 2,3 8,3,8 64,7 ES Madrid Cooling,,,,, 3,2 9,7 8,3 2,1,,, 23,2 11,7 kwh/m 2 DHW 1,9 1,7 1,9 1,9 1,9 1,9 1,9 1,9 1,9 1,9 1,9 1,9 22,7 Heating 11, 7,2 5, 1,7,2,,,, 1,5 4,1 9,5 4,3 IT Rome Cooling,,,,, 3,4 9,2 8,9 3,,1,1, 24,7 87,7 kwh/m 2 DHW 1,9 1,7 1,9 1,9 1,9 1,9 1,9 1,9 1,9 1,9 1,9 1,9 22,7 Heating 24,9 19,3 9,3 5,2,7,,,, 3,9 13,7 23, 99,9 IT Milan Cooling,,,,, 3,4 7,4 4,5,6,,,,9 139,6 kwh/m 2 DHW 2, 1,8 2, 2, 2, 2, 2, 2, 2, 2, 2, 2, 23,8 Heating 28,4 18,8 13,1 4,3 1,6,4,3,5 1,5 6,8 17,2 26,1 118,9 RO Bucharest Cooling,,,,, 5,3 8,7 6,6,,,, 2,6 163,8 kwh/m 2 DHW 2,1 1,9 2,1 2, 2,1 2, 2,1 2,1 2, 2,1 2, 2,1 24,3 Heating 27,8 22,5 13,4 6,7 2,2 1,2,9,9 1,9 7,4 18,1 28, 131, AT Vienna Cooling,,,,,, 3,9 3,5,,,, 7,4 162,6 kwh/m 2 DHW 2,1 1,9 2,1 2, 2,1 2, 2,1 2,1 2, 2,1 2, 2,1 24,3 Heating 28,7 24,9 18,8 1,3 3,,5,, 2,1 9,2 2,8 27,6 146, FR Paris Cooling,,,,,, 1,8 1,2,,,, 3, 173,3 kwh/m 2 DHW 2,1 1,9 2,1 2, 2,1 2, 2,1 2,1 2, 2,1 2, 2,1 24,3 Heating 24,3 19,8 13,3 6,8 2,6,7,9,7 2,2 8, 17,5 21,1 117,9 CZ Prague Cooling,,,,,,8 1,4 1,4,,,, 3,6 145,8 kwh/m 2 DHW 2,1 1,9 2,1 2, 2,1 2, 2,1 2,1 2, 2,1 2, 2,1 24,3 Heating 26,2 23,5 16,5 8,2 3,5 1, 1,1 1,1 2,3 8,8 19,,8 136,8 DE Berlin Cooling,,,,, 2,2 2,6 1,6,,,, 6,4 167,5 kwh/m 2 DHW 2,1 1,9 2,1 2, 2,1 2, 2,1 2,1 2, 2,1 2, 2,1 24,3 Heating,3 21,8 16,8 8,1 2, 1, 1,2,7 3,1 1,3 21,4,3 137,1 FI Helsinki Cooling,,,,,,,,8,,,,,8 163,6 kwh/m 2 DHW 2,2 2, 2,2 2,1 2,2 2,1 2,2 2,2 2,1 2,2 2,1 2,2,7 Apartment block 68

69 Table 12: Summary of simulated energy needs for heating, cooling and DHW for the office building base cases. Target Reference Country weather end-use Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual TOTAL EN Heating 8,7 5,3 3, 1,5,3,,,,,2 4,2 7,2 3,5 ES Seville Cooling,1,3 1,3 1,4 3,3 9,1 18,3 14,8 1,9 4,3,5,1 64,3 12,4 kwh/m 2 DHW,6,6,6,6,6,6,6,6,6,6,6,6 7,6 Heating 22,7,7 9,9 7,7,8,3,1,1,5 3,6 11,6 21,4 94,4 ES Madrid Cooling,,,2,5 1,9 5,5 11,7 1,9 4,6,8,1, 36,2 138,8 kwh/m 2 DHW,7,6,7,7,7,7,7,7,7,7,7,7 8,3 Heating 18,2 12,6 9,3 3,4,5,,,, 2,6 7,5 14,9 68,9 IT Rome Cooling,,,, 2,7 9,3 17,1 16,5 1,1 3,5,1, 59,2 136,4 kwh/m 2 DHW,7,6,7,7,7,7,7,7,7,7,7,7 8,3 Heating 3,3 24,8 12,6 6,6,9,,1,,3 4,9 17, 28,2 1,7 IT Milan Cooling,,,,1 1,5 7,9 13,7 9,3 3,9,4,, 36,6 171,3 kwh/m 2 DHW,8,7,8,7,8,7,8,8,7,8,7,8 9, Heating 29,9 2,5 14,1 3,1,6,,,,5 6,1 17,3 26,5 118,6 RO Bucharest Cooling,,,,2 3,4 7,4 12,7 1,1 2,4 1,2,, 37,4 165,3 kwh/m 2 DHW,8,7,8,8,8,8,8,8,8,8,8,8 9,3 Heating 4, 33,7 23, 11,1 2,,3,,1 1,7 11,8 26,7 39,3 189,7 AT Vienna Cooling,,,,1,8 2, 5,7 4,6,3,,, 13,5 212,5 kwh/m 2 DHW,8,7,8,8,8,8,8,8,8,8,8,8 9,3 Heating 32, 28,4 22,2 12,2 3,5,8,1,1 2,5 11,1 23,7 3,8 167,4 FR Paris Cooling,,,,,3 1,6 3,8 3,5,5,1,, 9,9 186,6 kwh/m 2 DHW,8,7,8,8,8,8,8,8,8,8,8,8 9,3 Heating 36,8 32,2 23,9 13,2 4,9 1,5,4,6 4, 14,4 27,2 32, 191,1 CZ Prague Cooling,,,,,8 1, 2, 2,2,2,,, 6,1 26,6 kwh/m 2 DHW,8,7,8,8,8,8,8,8,8,8,8,8 9,3 Heating 3,4 28,2 19,6 9,5 3,3,2,1, 1,4 9,2 21,9 28,4 2,2 DE Berlin Cooling,,,,,5 2,3 3, 2,5,4,,, 8,7 17,2 kwh/m 2 DHW,8,7,8,8,8,8,8,8,8,8,8,8 9,3 Heating 42,8 39, 33,1 18,7 6,1,9,3,6 6,7 19, 35,8 41,6 244,7 FI Helsinki Cooling,,,,,1,4 2,3 1,8,,,, 4,7 9,6 kwh/m 2 DHW,9,8,9,8,9,8,9,9,8,9,8,9 1,2 Office 69

70 Table 13: Summary of simulated energy needs for heating, cooling and DHW for the school building base cases. Target Reference Country weather end-use Jan Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Annual TOTAL EN Heating 12, 8,7 4,6 2,5,3,,,,,3 6,5 8, 42,9 ES Seville Cooling,,,1,4 1,6 8,4 19,4 2,2 1,7 2,6,2, 45,6 98,3 kwh/m 2 DHW,8,7,8,8,8,8,8,8,8,8,8,8 9,7 Heating 23,8 18,9 11,9 1, 1,1,3,1,,4 4,5 14,1 2,6,6 ES Madrid Cooling,,,,1,6 4,2 11,7 1, 3,4,1,, 21,2 137,5 kwh/m 2 DHW,9,8,9,9,9,9,9,9,9,9,9,9 1,6 Heating 2,5 16,7 12,2 6, 1,,,,, 3,3 1,3 14,3 84,4 IT Rome Cooling,,,, 1,4 8, 18,1,8 9, 2,5,2, 4,1 135,1 kwh/m 2 DHW,9,8,9,9,9,9,9,9,9,9,9,9 1,6 Heating 36,6 32,4 17,5 11,5 1,9,1,1,,5 8,6 23,4 31,2 163,9 IT Milan Cooling,,,,,5 6,7 13,5,3 2,4,1,, 23,5 198,9 kwh/m 2 DHW 1,,9 1,,9 1,,9 1, 1,,9 1,,9 1, 11,6 Heating 33,3,9 17,1 6, 1,2,,,,8 8,5 21,6 27,7 142,1 RO Bucharest Cooling,,,, 2, 5,5 11,7,1 1,2,5,, 21, 175,1 kwh/m 2 DHW 1,,9 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 11,9 Heating 46,4 42,3 28,6 17,2 5,4 1,9,1, 4,3 17,7 34, 42,8 24,8 AT Vienna Cooling,,,,,3 1,4 4,5,,1,,, 6,4 9,1 kwh/m 2 DHW 1,,9 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 11,9 Heating 32,9 31,1 23,5,7 5,2 1,5,2, 3,4 13,8 26,3 28,7 182,3 FR Paris Cooling,,,,,1,6 2,6,,3,,, 3,6 197,9 kwh/m 2 DHW 1,,9 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 11,9 Heating 37,8 35,6,5 16,9 7,4 3,1 1,,1 6,2 17,8 3, 31,1 212,6 CZ Prague Cooling,,,,,2,3,9,,,,, 1,4 226, kwh/m 2 DHW 1,,9 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 11,9 Heating 38,6 38,4 27,5 18,7 7, 1,1,4,1 4,4 16,5 31,2 33,1 216,9 DE Berlin Cooling,,,,,1 1,5 1,3,,1,,, 3, 231,8 kwh/m 2 DHW 1,,9 1, 1, 1, 1, 1, 1, 1, 1, 1, 1, 11,9 Heating 39,1 37,5 3, 19,5 6,6,9,3,1 7,7 18,6 32,9 33,6 226,8 FI Helsinki Cooling,,,,,, 1,1,,,,, 1,2 241,1 kwh/m 2 DHW 1,1 1, 1,1 1,1 1,1 1,1 1,1 1,1 1,1 1,1 1,1 1,1 13,1 School 7

71 3. Comparison of the EnergyPlus results with the simply hourly EN1379 and INVERT/EE-Lab approach In this chapter we compare the energy needs for heating and cooling of different buildings (and climate zones), which we derived from three different calculation tools:, applying a full dynamical approach (only sensible cooling loads) A simple hourly three-nodes model implemented as a spreadsheet tool (in accordance with the EN 1379) INVERT/EE-Lab Model with the underlying quasi-steady-state monthly energy balance approach Due to the different calculation approach and the different degree of the complexity of the building and usage description in the three simulation tools, the energy needs vary. Although the three models are using the same raw climate data, a different implementation of converting solar radiation on a horizontal plane into a specifically oriented vertical plane, result in deviating solar gains. In order to reduce these effects, we used the solar radiation data derived by the EN 1379 spreadsheet tool also for the INVERT/EE-Lab calculations. However they do not exactly match with those used for the EnergyPlus calculations. The EnergyPlus model calculates dynamic solar energy transmittances values (g-values) depending on the orientation, date and time. The EN 1379 spreadsheet tool is able to distinguish between orientation-depending g-values, in the INVERT/Model a single value per building needs to be defined. The results from the EnergyPlus model are derived using a complex ventilation control, which dynamically varies the ventilation rate depending on indoor air condition and present number of persons per square meter. In addition, deviating ventilation rates were defined for different usage types of rooms (e.g. classrooms, offices, conference rooms, aisles, stairways, baths, etc.). In the applied EN 1379 spreadsheet tool the ventilation rate can be hourly distinguished (for the whole building), in the INVERT/EE-Lab model the ventilation rate distinguishes between day and night times, non-using hours on using days and nonusing days. The applied shading schedule in the EnergyPlus model depends on the room depended indoor light intensity. In the two other tools a static approach using default values is used. The same holds for the internal gains due to lighting. In the two other models, the internal gains are fixed and independent from the heating or cooling needs of the building at a given time period. The EN 1379 spreadsheet tool doesn t distinguish between using and non-using days. This is one of the main reason, why the results from this tool for buildings which aren t used every day significantly deviate from those derived with the two other tools. For the non-commercial buildings (offices and schools) the EnergyPlus results are also including the latent heat (although it changes results only slightly), for all other results the sensible heat is considered only. In contrast to the dynamic approaches using thermal coupled nodes, in the quasi steadystate method the same indoor temperature (set temperature) is used for all relevant components such as: air, wall indoor surface temperature or indoor surface temperature of 71