Characteristics of a database for energy performance certificates

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Energy Procedia 132 Energy Procedia 00(2017) (2017)1000–1005 000–000 www.elsevier.com/locate/procedia

11th Nordic Symposium on Building Physics, NSB2017, 11-14 June 2017, Trondheim, Norway

Characteristics of a database for energy performance certificates The 15th International Symposium on District Heating and Cooling

Prieler M., Leeb M., Reiter T.

Assessing the feasibility of using the heat demand-outdoor University of Applied Science Salzburg - Department of Smart Building and Smart City, Markt 136a,5431 Kuchl, Austria temperature function for a long-term district heat demand forecast Abstract a

I. Andrića,b,c*, A. Pinaa, P. Ferrãoa, J. Fournierb., B. Lacarrièrec, O. Le Correc

IN+ Center for Innovation, Technology and Policy Research - Instituto Superior Técnico, Av. Rovisco Pais 1, 1049-001 Lisbon, Portugal

b Veolia constructed Recherche & buildings Innovation,are 291bound Avenueon Dreyfous Daniel, 78520 Limay, France Funded reconstruction and newly the issue of an Energy Performance Certificate (EPC) in c Département Systèmes Énergétiques Environnement - IMTSalzburg Atlantique, 4 rue Alfred Kastler, 44300 Nantes, France Austria. Due to that fact, there is a large pool ofetEPC. For the county around 51.000 EPC are listed in the so called ZEUS database by the effective date 31.12.2016. A focus of the study is the examination of the reconstruction’s impact on the buildings energy relevant values and evaluation of the characteristics of the database. In context of the examination of the reconstruction’s impact, those buildings for which both an Abstract EPC before and after reconstruction is available, are filtered and statistical analyzed. Before the statistical evaluation a plausibility check of the EPC was done. Based on this pre-check there are plenty of findings where problems occurred during the generation ofDistrict the EPCheating as wellnetworks as in the structure of ZEUS. are commonly addressed in the literature as one of the most effective solutions for decreasing the The amount of EPC in ZEUS willthe increase steadily. the future therehigh willinvestments be a much higher of EPC available greenhouse gas emissions from building sector.Hence Theseinsystems require whichamount are returned through the for heat such To improve quality and of the database and the opportunities generateininsights fromcould the data, the sales.statistical Due to analysis. the changed climate the conditions building renovation policies, heattodemand the future decrease, knowledge this statistical return analysis is taken into account for the further development of ZEUS. Thereby the focus is to determine prolongingofthe investment period. which variables should be implemented thefeasibility database of in using the future. As demand example–the layerstemperature of all the structural components of The main scope of this paper is to assessinthe the heat outdoor function for heat demand buildings be listed the database. Hence in the future it will be to amonitor a highThe amount of EPC where building forecast. should The district of inAlvalade, located in Lisbon (Portugal), waspossible used as case study. district is consisted of 665 components havevary changed in the context ofperiod reconstruction. buildings that in both construction and typology. Three weather scenarios (low, medium, high) and three district A general aim is the development of a database which can be used to evaluate the effectiveness of the different funding systems in renovation scenarios were developed (shallow, intermediate, deep). To estimate the error, obtained heat demand values were the field of reconstruction and adaption of the building technology. Consequently, the custody of EPC and statistical analysis will compared with results from a dynamic heat demand model, previously developed and validated by the authors. be available as basis for political decisions. The results showed that when only weather change is considered, the margin of error could be acceptable for some applications (the error in annual demand was lower than 20% for all weather scenarios considered). However, after introducing renovation © 2017 The Authors. Published by Elsevier Ltd. scenarios, the error value increased up to 59.5% (depending on the weather and renovation scenarios combination considered). Peer-review under responsibility of the organizing committee of the 11th Nordic Symposium on Building Physics. The value of slope coefficient increased on average within the range of 3.8% up to 8% per decade, that corresponds to the decrease in the number of heating hours of 22-139h during the heating season (depending on the combination of weather and Keywords: Energy Performance Certificate; Reconstruction; Heating demand; U-value; Database for Energy Performance Certificates renovation scenarios considered). On the other hand, function intercept increased for 7.8-12.7% per decade (depending on the coupled scenarios). The values suggested could be used to modify the function parameters for the scenarios considered, and improve the accuracy of heat demand estimations.

© 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling. Keywords: Heat demand; Forecast; Climate change 1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the organizing committee of the 11th Nordic Symposium on Building Physics.

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the Scientific Committee of The 15th International Symposium on District Heating and Cooling.

1876-6102 © 2017 The Authors. Published by Elsevier Ltd. Peer-review under responsibility of the organizing committee of the 11th Nordic Symposium on Building Physics 10.1016/j.egypro.2017.09.704

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1. Introduction The Energy Performance Certificate (EPC) parameter heating demand has almost become an everyday term, based on the fact this value has to be mentioned in any real estate listing based by the Austrian law [1]. Every new building and funded reconstruction is bound on the issue of an EPC. So there is a large pool of EPC. For the county of Salzburg, the EPC are listed in the ZEUS database. Salzburg was the first of all the counties which realized the requirement to have a database to handle the residential building subsidies data flow. For this purpose, the ZEUS database was invented. Nowadays ZEUS is used in two other counties, namely Carinthia and Styria [2]. For each EPC different data files are stored in ZEUS, always a pdf, a xml and source file of the EPC calculation program [3]. The pdf is the official EPC document which is handed over to the client [2]. The xml does not include all the data which are shown in the pdf but basic information [3]. Sometimes besides this the EPC calculator also uploads pictures, construction plans or other documents to ZEUS. The uploaded files are used for examining the correctness of the EPC and especially the energetic values regarding observance with requirements by law resp. building subsidies. The access to ZEUS is limited to following groups: EPC calculator, planer, developer, affected departments of the regional governments and persons who do statistical analyses of the data from EPC [3]. Only groups members of defined profession by law are allowed to create an EPC, e.g. architects, consulting engineers for building physics, building technology and so on [4]. Nowadays five [3] different EPC calculation programs exist in Austria. Over the last ten years in the time period 2006 to 2016 around 51.000 EPC were uploaded to ZEUS in Salzburg, this is the data basis for this study. The authors do not have access to the EPC uploaded in the other counties. The focus of the study is the examination of the reconstruction’s impact on the buildings energy relevant values. In this context, those buildings for which both, an energy performance certificate before and after reconstruction is available, are filtered and statistical analyzed. For this study only the EPC of residential buildings are considered, this includes single- family house, terraced houses, duplex, multi-family house and apartment blocks. The limitation was done because there are different operational profiles deposited with the non-residential building and residential buildings. The operational profiles have an impact on the calculated energetic values within the EPC, which are looked at in this study. For this reason, a germinal consideration was not seen as an appropriate method. This work differs from other statistics on EPC, such as [2,5] and [6] to the effect that the focus here is the comparison of the reconstruction to inventory. Also the considered population differentiate. In this work the EPC of the province of Salzburg are analyzed. Furthermore, this study distinguishes from [7] and [8] due to the high amount of analyzed EPC concerning the existing building stock. The distinction to [9] and [10] is the expanded period under observation and the different research methodology. Based on the fact that the amount of EPC in ZEUS will increase steadily another aim of this study is to determine which development at ZEUS are necessary to get more informative results by statistical analysis. In this context it is investigated which variables should be implemented in the database. A future perspective of the usage of ZEUS shall be that it is a database which can be used to evaluate the effectiveness of the different funding systems in the field of reconstruction and adaption of the building technology. Consequently, the custody of EPC and statistical analysis will be available as a basis for political decisions. 2. Methods and data basis For the data analysis a xml-export of all the EPC uploaded in the year 2006 -2016 in Salzburg was done. In several cases EPC are multiple times uploaded into the ZEUS database for different purposes, e.g. for different funding programs. Also there are EPC which are not plausible in ZEUS. Consequently, it is necessary to make a pre-analysis of the data before starting the actual data analysis. Concerning the plausibility check of the data a model was developed, which restricts the range of values. In this context it was determined which values are possible and plausible for each variable. In sum there are 323 variables in the structure of xml 4.0, which is the actual xml-version of ZEUS. The stored information in the xml includes parameter for management of the EPC in ZEUS, input parameters and output of the EPC calculation program. As example for management parameters, it is shown when the EPC was created and uploaded, for which purpose (building method, residential building subsidies, etc.) and a given individual ID of the EPC and for the building based on the address. Information about the calculator and the building owner as

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well as the climate and the heating system are examples for input parameters. Output parameters are calculated values stored in the xml, like the heating demand, the average U-value and the calculated heating load. A description of the structure in detail would exceed the scope of this paper. In this study two output variables of the EPC, the heating demand and the U-value as well as one input variable – the used energy carrier – are analyzed. The comparison of inventory and reconstruction in this work only includes the EPC before reconstruction (inventory) to the EPC after reconstruction (reconstructed) of residential buildings. Therefore, it was necessary to identify related EPC of each building – EPC inventory and EPC reconstructed. This identification was done with the variable of the object-ID. The object ID is created by ZEUS based on the address of the building. By filtering this data, the data basis for the statistical analysis is reduced to 5,290 EPC (2,645 inventory and 2,645 reconstructed). The comparison with the construction (new building) is carried out with all the latest valid EPC from new buildings. In the event that several EPC are available for a construction object, the newer EPC is taken into consideration (= last valid). The number of latest valid new building EPC is 3,624. For the description and analysis of the data, methods from the field of descriptive statistics are used and therefore the programs IBM SPSS Statistics 24 and MS Office Excel 2016. 3. Statistical analyses The heating demand is a frequently used parameter for the evaluation of the energy performance of buildings. It can be distinguished between two bases, namely reference climate and location climate. In this work only the heating demand at the location climate is considered based on its importance in the context of the zoning law and the residential building subsidies. The comparison of the specific heating demand between inventory and reconstruction is shown in Figure 1 with histograms, where (a) shows the heating demand before reconstruction and (b) after reconstruction. The specific heating demand is illustrated at the x-axis, while the y-axis shows the frequency of the values. For the purpose of better illustration only values of the heating demand in the range 0 - 500 kWh/m².a are shown. There are in sum nine extreme outliers, such as the maximum of 772 kWh/m².a of the inventory, which are not seen based on this cut. The cut was done at 500 kWh/m².a because this nearly matches the border between moderate und extreme outlies. Beyond this value there are only three extreme outliers (481, 486 and 496 kWh/m².at). The credibility of each outlier was proofed, mostly they are from historic buildings in the center of Salzburg. (a)

Median: 154 Mean: 168 SD: 78 N: 2,645

Fig. 1. Heating demand before reconstruction (a) and after reconstruction (b)

(b)

Median: 72 Mean: 87 SD: 47 N: 2,645

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Looking at the histogram for inventory in Fig 1 it can be observed that the heating demand is widely spread, mostly in the ranges between 70 kWh/m².a and 240 kWh/m².a. After reconstruction the heating demand has obviously been reduced and is located mainly in the field between 40 kWh/m².a to 110 kWh/m².a. The median of the heating demand is 154 kWh/m².a for inventory and 72 kWh/m².a. after reconstruction. So the heating demand has more than halved in the course of reconstruction. In comparison: the median of the heating demand of new buildings is 39 kWh/m².a. The U-value is a relevant parameter for the characterization of the quality of the outer casing and therefore for the construction technology. During the reconstruction, the average U-value has considerably reduced. The distribution of the U-values for inventory, reconstruction and for new buildings can be seen in Figure 2. It should be noted that the scale for the average U-value (y-axis) was limited to 2.2 W/m².K for better illustration. Above this value there are only three extreme outliers with 2.28, 2.38 and 3.42 W/m².K. The EPC for inventory with mean U-value of 3.42 is from a traditional building of the historic center of Salzburg established at the end of the 19th century. The lower whisker at the box plots in Figure 2 show the smallest U-value by the different EPC. The beginning of the box indicates the lower quartile and therefore the position of the 25 % lowest values. The line within the box shows the location of the median and the end of the box the upper quartile. The rings and stars above the upper whisker are outliers, rings are moderate outliers and stars are extreme outliers. While the median of the U-value before reconstruction was 0.82 W/m².K, it decreased to 0.38 W/m².K after the reconstruction and has more than halved. In comparison, the median of the average U-value for new buildings is 0.25 W/m².K. A difference between reconstruction and new building is clearly in the range of the reached U-values. After the reconstruction the U-values is in the range of 0.20 W/m².K to 0.81 W/m².K and for new construction in the range 0.17 W/m².K to 0.33 W/m².K without statistical outliers. A reason for this result can be the stricter regulations by law, funding guidelines by new buildings as well as the different opportunities for constructional accomplishment. During the reconstruction the used energy carrier has changed for 14 % of the buildings. In this context it must be mentioned that changes in the heating system are not bound to creating an EPC. The most widely used energy carrier in the considered data is oil, followed by biomass. This applies to both, inventory and after reconstruction (see Fig. 3). After reconstruction other energy carriers are often used instead of oil for generating the needed thermal energy. At inventory 41 % of the heating systems were operated with fuel oil, after the reconstruction, the percentage has been reduced to 31 %. The share of biomass has increased from 18 % to 22 %. In addition to biomass also a significant increase in district heating and natural gas compared to the initial situation are determined. Before reconstruction Median: 0.82 Mean: 0.87 SD: 0.30 N: 2,645 After reconstruction Median: 0.38 Mean: 0.45 SD: 0.20 N: 2,645 New Building Median: 0.25 Mean: 0.25 SD: 0.04 N: 3,624

Fig. 2. U-value before reconstruction, after reconstruction and new constructed building

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1200 1000

Frequency

800

600 400 200

0

Biomass

Electricity

Electricity Heat Pump

District heating

Before reconstruction

Natural gas

Oil

Coil

Other

After reconstruction

Fig. 3. Used energy carrier before and after reconstruction

An increase is also reflected in electricity. The change in power is due to the increased use of heat pumps. After reconstruction nearly three time more heat pumps are used for heating purpose than before. On the contrary the use of electricity for direct electricity heating systems and night storage electricity heating systems has reduced significantly. A main difference between direct electricity system and night storage heaters is the time period when the electricity is consumed in order to produce heat. Night storage heaters consume electricity at low load periods when the electricity tariff is keen, commonly at night and store the heat for several hours. For night storage heater a power meter is needed which distinguish between the electricity consumption at time of normal tariff and low tariff (night tariff). While in former days, when there was a big difference between the tariffs, night storage heaters were quite popular. Nowadays the night tariff is not much cheaper than the normal tariff. Furthermore, today there are many electricity suppliers which do not even offer a night tariff. 4. Conclusion and Outlook The study showed the impact of reconstruction on energetic values by comparing the EPC before and after reconstruction of more than 2,645 residential buildings. An evaluation of reconstruction in this way with such a high amount of datasets was done in this study the first time. Reconstruction has a significant impact on the energy values of buildings. In line with this, the U-value of the compared residential buildings reduced by more than half, related to reconstruction. Hence there is a significant reduction of the heating demand of the buildings after reconstruction too. A look at the used energy carrier shows that reconstruction cannot only be linked to insulation work. During the reconstruction, the used energy carrier changed for 14 % of the buildings. Before reconstruction more than 40 % of the analyzed residential buildings had implemented an oil heater. After reconstruction in particular, biomass, district heating, natural gas and electricity for the operation of heat pumps are increasingly used for the production of thermal energy. When interpreting the results, it should be noted that only those residential buildings were evaluated, where an EPC before and after reconstruction was available in ZEUS. Inventory (before reconstruction) is not necessarily equal to the original state at the time of construction. It is quite conceivable that the existing building was reconstructed before. In this context it should be mentioned that with reconstruction individual measures are quite common in Austria [11].

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In the past there was no obligation to upload each EPC in the ZEUS database. So it has to be assumed that not each EPC exists in ZEUS. However, if there was an application for funding of the reconstruction, the EPC is available in ZEUS, thus is considered in this data analysis. Last but not least it has to be taken into account that measures such as changing the heating system are not bound on the issue of an EPC. As part of the data analysis, the boundaries of the ZEUS could be identified. To improve the quality of the database and the opportunities to generate insights from the data, the knowledge of this statistical analysis is taken into account for the further development of ZEUS. With the actual version of the database ZEUS it is possible to get an overview on the energetic values of the buildings. For reconstruction, it could be of interest in which way the building has changed exactly. In this context it is necessary to have variables for the layers of all the structural components of buildings. With the additional information it is possible to monitor how the reconstruction was done in detail, meaning which materials were used in order to reach the U-value. Furthermore, the information which residential building subsidies and building law was effective when the EPC was uploaded is a needed information for evaluating the political decisions. With this additional information the EPC database and statistical analysis of it can give input for politics. Acknowledgements The data and the basis of this summary are from the research project "Alternative Wege zum Nullenergiehaus" with partial funding of the Trans4Tec program, Salzburg. The authors are grateful to the Government of Salzburg and gizmocraft, design and technology GmbH, that ZEUS could be used as data basis. References [1] Bundesgesetz über die Pflicht zur Vorlage eines Energieausweises beim Verkauf und bei der In-Bestand-Gabe von Gebäuden und Nutzungsobjekten (Energieausweis-Vorlage-Gesetz 2012 -EAVG 2012), BGBl. I Nr. 27/2012. [2] Amtmann A. TABULA: Reference buildings – The Austrian building typology. Scientific Report D 6.9; 2010. http://www.buildingtypology.eu/downloads/public/docs/scientific/AT_TABULA_ScientificReport_AEA.pdf [3] Gizmocraft design and technology GmbH. Online Datenbank zur Verwaltung von Energieausweisen: FAQ; 2017. https://www.energieausweise.net/faq#a3 [4] Wirtschaftskammer Österreich. Befugnis zur Erstellung von Energieasuweisen; 2016. https://www.wko.at/service/wirtschaftsrechtgewerberecht/Die_Befugnis_zur_Erstellung_von_Energieausweisen.html [5] Brunn M., Liepert M. Energieausweiszentrale Jahresbericht 2012; https://www.eawz.at/Daten/Bilder/Infos/EAWZ_Jahresbericht_2012.pdf [6] Dascalaki E. G., Kontoyiannidis S., Balaras C. A., Drouttsa K. G. Energy verification of Hellenic buildings: First findings; Energy and Buildings, Volume 65; 2013. p. 429-437, [7] Dascalaki E. G., Droutsa K., Gaglia A. G., Kontoyiannidis S., Balaras S. A. Data collection and analysis of the building stock and its energy performance – An example for Hellenic buildings. Energy and Buildings, Volume 42; 2010. p. 1231-1237. [8]. Loga T., Popiolek M., Cohen R., Corrrado V., Dascalaki E., Cyx W., Geissler S., Zavrl M., Panos C., Hanratty M., Georgiev G. Datamine: Collecting Data from Energy Certification to Monitor Performance Indicators for New and Existing buildings; 2009. https://ec.europa.eu/energy/intelligent/projects/sites/iee-projects/files/projects/documents/datamine.pdf [9] Prieler M., Leeb M., Reiter T. Sanieren in Salzburg – Analyse der Energieausweise vom Bundesland Salzburg der Jahre 2006 – 2014. In: International Conference on high quality retrofit and redensification with timber construction systems. Graz, Austria; 2015 [10] Prieler M., Leeb M., Reiter T. Sanierung und Gebäudetechnik - Analyse der Energieausweise vom Bundesland Salzburg der Jahre 2006 – 2015. In: e-nova 2015: international congress for sustainable buildings. Pinkafeld, Austria; 2015. [11] Oberhuber A., Denk D. Zahlen, Daten, Fakten zu Wohnungspolitik und Wohnungswirtschaft in Österreich; Endbericht; 2014. http://www.bmwfw.gv.at/Wirtschaftspolitik/Wohnungspolitik/Documents/Zahlen%20Daten%20und%20Fakten%20-%20Endbericht.pdf