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Evaluation of the space heating need in residential buildings at territorial scale: The case of Canton Ticino (CH)
Energy and Buildings 148 (2017) 218–227
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Evaluation of the space heating need in residential buildings at territorial scale: The case of Canton Ticino (CH) Luca Pampuri a , Nerio Cereghetti a , Pamela Galbani Bianchi a , Paola Caputo b,∗ a Istituto Sostenibilità Applicata All’ambiente Costruito, Scuola Universitaria Professionale della Svizzera Italiana, Campus Trevano, Via Trevano, CH-6952 Canobbio, Switzerland b Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Via Bonardi 9, 20133 Milano, Italy
a r t i c l e
i n f o
Article history: Received 24 October 2016 Received in revised form 13 February 2017 Accepted 21 April 2017 Available online 26 April 2017 Keywords: Heating Residential buildings Energy performance estimation Energy renovation
a b s t r a c t Due to the energy and environmental emergencies, in the last decades, goals, standards, regulations and incentives have been defined according to governmental strategies and regulations at national or local level. Referring to the built environment, this implies a knowledge of the energy performance of the building stock that instead is still poor. A method able to characterize the energy performance of the built environment at territorial scale was developed. It follows a simplified procedure that takes into account: data available in the Energy Certificate of Buildings database, data about the age of the buildings and the energy reference surfaces available in official statistics database. The method was applied to residential buildings in Canton Ticino, in southern Switzerland. The results achieved were compared to information available in the Cantonal Energy Balance, with regards to gas fueled residential buildings. The comparison reveals the global reliability of the method if applied at territorial scale. This method could support energy policies at regional/urban level, in order to stimulate a rational and massive refurbishment of the built environment. Further improvements are in progress in order to provide a territorial representation of results by a Geographic Information Systems (GIS) tool. © 2017 Elsevier B.V. All rights reserved.
1. Introduction The path to carbon neutral communities represent long lasting and urgent challenges for all governments. For example, the EU has approved the “Roadmap for moving to a competitive low carbon economy in 2050” [1] with the objective to reduce greenhouse gas emissions by 80–95% by 2050 in comparison to those of 1990. Analogously Switzerland has approved the Energy Strategy 2050 that includes the main aims for the energy policy towards 2050 [2]. Energy statistics show that European and Swiss buildings are responsible of more than the 40% of the total final energy consumption [2,3], representing an important energy saving opportunity. More in details, energy statistics show that buildings space heating accounts for 68% and 71% of end-use energy consumption respectively in Europe and Switzerland, while lighting and electrical appliances account for 15%, water heating for 12% and cooking for 4% [2,3]. These information underline the importance to oper-
∗ Corresponding author. E-mail address: [email protected] (P. Caputo). http://dx.doi.org/10.1016/j.enbuild.2017.04.061 0378-7788/© 2017 Elsevier B.V. All rights reserved.
ate on the thermal final uses in buildings, with particular regard to those built during times when the requirements on energy performance were much less restrictive. Previous researches [4–6,23] stressed the importance to know which buildings mainly affect energy consumption and environmental problems and to investigate the effects that energyretrofitting programs may have in the future. These researches have explained that the analysis of the energy flows in buildings can be described by top-down building stock energy models or bottom-up models. The first models study energy flows among a defined territory and then deepen the analysis of smaller parts of the existing building stock. Reinhart and Cerezo Davila [7] explained that these models are suitable for analyzing the global energy flows of a large building stock (e.g. energy consumptions by fuel or by sector for a defined territory) and for developing and simulating future scenarios. Conversely, these models are less suitable for focusing the evaluations on a specific neighborhood or for investigating detailed figures (e.g. types of final energies and their distribution among a territory). The bottom-up approach adopts input data from a lower level (such as individual or groups of buildings) and extrapolates the results for the whole sector according to the representative
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weight of the sample considered. Reinhart and Cerezo Davila [7] indicated that “bottom-up urban building energy models (UBEMs)” are more suitable at neighborhood scale, ranging from several dozen to thousands of buildings. They also stressed the features of “building energy models (BEMs)” at the individual building level and the limit related to the discrepancies between simulated and measured energy consumptions, that are mainly due to the uncertainty related to infiltration rates, equipment loads and occupants behavior. The scientific community recognizes that in the last years several BEMs have been developed and refined. They have become increasingly precise and accurate, often equipped by a user-friendly interface. However, their use requires a good knowledge of buildings physics; detailed information about envelope, systems and plants of the buildings; sufficient time to input data and to verify results; and, consequently, a consistent budget to activate these competences. Therefore, these methods and tools are applicable to small group of existing buildings, so their application cannot be considered as a pivotal support in energy planning and energy policies definition. Therefore, we can state that large building stock evaluations still represent a challenging topic and are currently covered by many studies aimed at investigating energy consumption mainly in the residential buildings sector. In addition to the interesting insights about how to synthetize building load profiles by UBEMs reported in Reinhart and Cerezo Davila [7], we can mention also the researches carried out by Swan and Ugursal [8], Theodoridou and Papadopoulos [9], Fracastoro and Serraino [10], Dall’O’ et al. [11], Ma et al. [12], Zhao and Magoulès [13], Hamilton et al. [14], Mata et al. [15], Mirakyan and De Guio [16], Ballarini et al. [17] and Caputo and Pasetti [6]. Many of these contributions suggest abstracting a building stock into building archetypes, an approach suitable for large-scale bottom-up building energy models. As reported in Reinhart and Cerezo Davila [7] and in Ballarini et al. [17], the definition of an archetype can be either based on a sample building (an actual building within the group that is documented by an audit or another detailed energy report) or on a virtual building which is based on statistical building data and/or expert opinion. The research here presented follows the same approach, where the sample buildings are those registered in the Cantonal Energy ® Certificate of Buildings (CECE ). Based on the method described in Section 2, the research investigates the heating needs in a large building stock (i.e. the residential buildings of Canton Ticino); demonstrates the reliability of the achieved results; and provides a first estimation of the benefits due to energy renovation measures. This last point represents a first contribution, to be faced more systematically in the further developments of the research, for defining energy retrofit scenarios able to support the development of massive and effective measures and policies.
1.1. Context: the case of Canton Ticino The context of application is the territory of Canton Ticino, a region of about 2800 km2 and 350,000 people in southern Switzerland (Fig. 1). Due to geographic position, in Canton Ticino various landscapes, morphologic and climatic conditions are present, so it can be consider a sample of different European regions (like northern Italy,1 southern Germany, part of Austria and France). Referring to winter conditions, the standard Degree-Days (DD) in Ticino range from 2326 in Lugano station to 5287 in the moun-
1 Not by chance, Insubria is a defined region that includes the Swiss Canton Ticino and parts of the Italian regions of Lombardy and Piedmont.
219
Fig. 1. Location of the territory under examination in the European context.
tain station of San Bernardino.2 Low elevation areas like Lugano has a temperate/continental climate, similar to the adjacent northern Italy, while high elevation zone have alpine climatic conditions. Further, due to history and level of development, the building stock of Canton Ticino presents architectural technologies, types of envelope and ages similar to those averagely present in Europe. For example, Canton Ticino has 125 people per km2 , little more than the EU average (113 people per km2 ) and quite similar to the Italian average (200 people per km2 ). If, as a collation, we compare residential buildings in Ticino to residential buildings in Italy, we find many similitudes. In fact, in both the cases, the most part of residential buildings are small and stand alone, about 2/3 of the buildings has one or two floors (69%3 and 67%4 in Swiss and Italy, respectively) and the most part is constructed by load-bearing wall. Furthermore, residential buildings built after 1990 represent respectively the 11% in Italy and the 16% in Swiss, evident signal of a great need of renovation. Analogous comparison can be reported also considering other adjoining European countries, such as Germany, France, Austria etc. However, unlike other European countries, Ticino (and other Cantons of Swiss) was less affected by the impact of the Second World War and of the socio-economic boom of the ‘60 s and ‘70 s on its building stock. In fact, in Ticino, residential buildings built before 1961 represent the 56% of the total (e.g. in Italy they are the 44%), while residential buildings built between 1961 and 1990 represent the 29% of the total (e.g. in Italy they are the 45%). Details about types of residential buildings and related ages in Ticino are reported in Sections 2.1 and 2.2. 2. Method In section 1, we concluded that a reliable estimate of heating needs in existing residential buildings is a key requirement in energy planning at territorial scale and to define effective energy policies. We underlined that top-down methods do not permit to achieve this knowledge and, on the other hand, bottom-up method in general provide trusted energy information only for small group of buildings. This knowledge building boosts researchers toward exploring new hybrid methodologies, such as that developed in the framework of the present research. More precisely, our aim is to develop a method for the characterization of the energy performance in terms of heating needs of a large building stock. This method is expected to be: simple, transparent, based on data avail-
2 Average DD based on monthly data of the last 30 years provided by the statistical office of Canton Ticino. 3 Data reported in RBD [18]. 4 Data reported in the Italian census (www.istat.it).
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able in official dataset and statistics, replicable, upgradable over time and compatible with GIS supported tools. The method integrates a bottom-up approach because of the selection of the sample buildings and of the analysis of their energy audits. Nevertheless, the method integrates also a top-down approach because of the investigation of the statistics information about the features of the entire building stock (e.g. use, age, energy reference surface, number of floors, address etc.) and because of the verification of the results by the comparison to global data available in the Cantonal Energy Balance, as reported in Section 3.1. 2.1. Available datasets In Swiss, the main information about the built environment are collected in the Federal Register of Buildings and Dwellings [18], a very rich, verified and updated dataset. In connection with the 2000 Population Census,5 the Federal RBD was created on the basis of the Housing Census carried out at the same time. The Federal RBD includes all buildings in Switzerland used for residential purposes and the dwellings therein. In addition to unambiguous and nationwide-unique building and dwelling identification codes, the Federal RBD records the most important basic data, such as address, location details, year of construction, number of floors, type of heating for the buildings, and for the dwellings, number of rooms, size (floor area), etc. The Federal Statistical Office (FSO) administers the Federal RBD in close cooperation with municipal construction authorities as well as with specialized bodies in the federal government, cantons and municipalities. The construction authorities notify the FSO of all building projects subject to construction approval (new buildings, conversions or renovations, demolitions) via the web, using designated interfaces or questionnaires. RBD [18] was deeply investigated with particular regard to the age and the energy reference surface of each residential building in Canton Ticino. RBD [18] classifies the age of construction of each building as follows: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.
before 1919; from 1919 to 1945; from 1946 to 1960; from 1961 to 1970; from 1971 to 1980; from 1981 to 1985; from 1986 to 1990; from 1981 to 1985; from 1986 to 1990; from 1991 to 1995; from 1996 to 2000; from 2001 to 2005; from 2006 to 2010; from 2011 to 2015; after 2015.
On the basis of statistical evaluations of the architecture technologies related to the different ages, the classes listed above were reduced to 9 age classes including, at least, a range of 10 years, as reported in the following Table 2. Since the RBD does not include the information about the energy needs of the buildings, another dataset was explored. Since the ® institution of the Cantonal Energy Certificate of Buildings (CECE ) in 2009, in Swiss a uniform method for the evaluation of the buildings energy performance was provided accordingly to the federal
norms. This method is aimed to support energy evaluations, both at the envelope level and at the building level (envelope, systems, ® plants, conditions of operation). Currently CECE is voluntary and is suitable for residential buildings, simple administrative buildings and schools. If a building has a mixed use (i.e. residential and commercial), it is possible to apply the CECE only if the residential part is more than ¾ of the total. ® The tool for the elaboration of CECE were developed by the ® 6 support of the MINERGIE Agency. The calculation is based on a steady state method based on monthly data, according to SIA7 380/1 [19] and SIA 2031 [20]. Climatic data for the evaluation are defined according to SIA 2028 [21], referring to the period from 1984 to 2003. ® In addition to CECE , it is possible to provide also a detailed ® advice report, called CECE Plus. This is a detailed energy report provided by an energy analyst. In details, the report includes a set of customized measures for improving the buildings energy efficiency, the technical, economic and financial details of the possible interventions and an expert support in realizing the building renovation. ® ® CECE and CECE Plus can be required by the owner or by the administrator of the building and can be compiled and pro® vided only by CECE certified experts. In order to complete the certification, they carry out an on-site inspection and an accurate examination of the building. The strict relationship between the owner of the building (that has to provide data about energy consumption, i.e. space heating, domestic hot water and electricity, at least for the last three years) and the energy expert (that starts the analysis with an on-site survey) is a pivotal point of the pro® cedure. In general, the CECE implies at least 5–10 working hours ® and a cost of at least 500 CHF, while the CECE Plus implies at least 15–25 working hours and a cost of at least 1500 CHF. ® The CECE report provides the energy certification of the building by the energy label. This is based on the calculated buildings energy needs that are then compared to actual energy consumption. After the calculation of the energy performance, it is possible to classify the buildings in 7 classes from A (the most efficient) to G (the least efficient). Table 1 reports the classification considering only the envelope performances. ® The classification provided by CECE is based on the contents of SIA 2031 [20]. In brief, for each building a limit value of the heating needs is calculated. This value is compared to the actual value of the analyzed building. The limit value corresponds to the interface between class B and C (100% of the limit), while the other classes are set by increasing or decreasing of 50% as reported in Fig. 2. Buildings with heating needs higher than 300% of the limit value are in class G. For example, if a building has a heating need equal to 130 kWh/m2 y and the limit value for that building (taking into account its climatic condition, geometry etc.) is 100 kWh/m2 y, the class is C, since 130/100 is included between 100% and 150% of the limit value. This means that the energy classes are defined building by building and there are not fixed range of heating needs for identifying the classes. A similar approach was adopted also in other energy certification procedures, such as that in force in Lombardy Region in northern Italy.8 ® The residential buildings of Canton Ticino registered in CECE represent a wide sample of buildings for which a reliable energy analysis is available and accessible, since the procedure briefly described imply a detailed energy audit, differently from other
6
www.minergie.ch (in Italian, French and German). SIA is the Swiss Society of Engineers and Architects (www.sia.ch). Among its aims, SIA provides norms and regulations about energy and buildings issues. 8 www.cened.it (in Italian). 7
5
www.bfs.admin.ch.
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Table 1 ® Energy classification at envelope level as provided by CECE (www.cece.ch).
Table 2 ® Number of residential buildings by age and by energy class (CECE database, update at end 2015). Energy class Age 1 2 3 4 5 6 7 8 9
A
Before 1919 1919–1945 1946–1960 1961–1970 1971–1980 1981–1990 1991–2000 2001–2010 After 2011 Total
2 1
2 34 39
B
C
D
E
F
G
total per age
total in classes E-F-G, %
13 6 10 16 14 4 1 3 11 78
11 4 14 10 24 49 10 5 2 129
16 8 6 13 34 95 11 2
24 5 14 35 47 81 6
26 11 33 45 51 22 4
122 70 142 177 111 12 4
185
212
192
638
212 104 221 296 282 263 36 12 47 1,473
81% 83% 86% 87% 74% 44% 39% 0% 0% 71%
Fig. 2. Classes of the Swiss energy classification.
types of databases of buildings energy classification. In other cases, in fact, lacks in uniformity, completeness and reliability of the energy certification makes this approach too susceptible to errors. For example, in the context of northern Italy, Dall’O’ et al. [22] provide a selection of the available energy certifications and a definition of a set of representative buildings in order to evaluate the energy performance of a defined building stock. In this case, the problem of the reliability of the available certificates was well stressed, evaluating the risk of managing wrong information or overestimated heating needs. In the same context, Khayatian et al. [23] underline the problem of defective input data in the energy cadaster of buildings in Lombardy, offer a method for detecting
anomalies in buildings energy certificates and try to define a correlation between quasi-steady-state and dynamic simulations. In our case, the certifications referred to buildings in progress were discarded because for these buildings it is not possible to carry out the final verification of the certification during the operation stage, ones completed and occupied. Cleared these considerations, in the present case, a sample of ® 1473 buildings extracted from CECE was taken into account, as described in Table 2. This sample includes single-family and multifamily buildings, located in all the climatic areas of Canton Ticino and represent the 1.4% of the overall residential buildings in the same Canton (108,064 residential buildings as reported in the Federal RBD at end 2015). The first results of the investigation are reported in Fig. 3 and Table 2. It is possible to observe that the most part of certified buildings belongs to ages from 1946 to 1990 that are also the ages with the lower energy performance (i.e. the highest yearly heating needs indexes). The last column of Table 2 reports, for each age, the percentage of buildings belonging to class E, F and G (i.e. that consume the double or more in respect to the reference building). On average the 71% of the total belongs to these classes and this percentage becomes less than the half only for buildings built after 1980. In our method, these buildings represent the sample buildings to be adopted as archetypes accordingly to Reinhart and Cerezo Davila [7]. In fact, in their building archetype definition, Reinhart and Cerezo Davila [7] report many studies in which type of use and age were taken into account in the so called segmentation phase (the phase of the selection of sample buildings able to represent the entire building stock). They also state that each sample building can represent less than 50 up to 500,000 buildings. The considerations mentioned in Section 1 allow us to argue that sample buildings
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Fig. 3. Distribution of the residential buildings in Canton Ticino (total in dark and certified in light) per ages, in percentage. ®
extracted from CECE can be considered as representative of the entire residential building stock, accordingly also to the verification reported in Section 3.1. All these buildings were grouped by age, following the age classification reported in Section 2.2, and the yearly heating needs index (in kWh/m2 y) of each was selected among the available data.
2.2. Estimation of the heating needs In Section 2.1 we concluded with the following selection of information useful in order to apply our method: the energy reference surface of each residential buildings (108,064 as extracted from RBD, 2015) grouped by age; the yearly heating needs index (in kWh/m2 y) of each residential buildings (1473 as extracted from ® CECE database, update at end 2015) grouped by age. Considering the overall residential building stock of Canton Ticino, it is possible to observe as the most part of the buildings were built between 1919 and 1960 (Fig. 4), attesting the old age of the stock and therefore the need of renovation. Further Fig. 4 underlines the low density of the building stock, providing an average ratio between energy reference and number of buildings of about 331 m2 /building. If a building (subscript j) has an energy certificate, it belongs both to the first and to the second dataset. Having the same identification code in both the datasets, we can estimate the space heating needs (Qj in kWh/y) of the j building as follows: Qj = EAj × EIj
(1)
where EAj is the energy reference surface of the j residential building in m2 and EIj is its yearly heating needs index in kWh/m2 y. ®
We stressed that the EI is available by the CECE database only for the certified buildings. Conversely, for the not yet certified buildings the method here presented provide an estimation of the EI based on the age of the building. In fact, the second step of the method provides the calculation of the average value of the EI for each class of age as follows: ®
1. The buildings certified by CECE are grouped by age (age i, i from 1 to 9, according to the classes of age listed in Section 2.1);
2. For each age i, average values of the EI are then calculated on the basis of the information available for the buildings certified by ® CECE following the equation: EI i =
j,i
EIj,i × EAj,i ⁄
j,i
EAj,i
(2)
where EIj,i is the yearly heating needs index of the j residential building belonging to the age i and EAj,i is the energy reference surface of the j residential building belonging to the age i; 3. The EI of the not certified buildings was estimates taking into account their age of construction and assigning the average EI calculated by Eq. (2). The scheme of the method (data collection and elaboration) is shown in Fig. 5. 3. Results On the basis of the described method, it was possible to associate an average yearly heating needs index, EI i , as reported in Fig. 6. The trend-line (based on the yearly heating needs index of certified buildings) underlines a progressive decreasing of heating needs with the age: the more the building is recent, the most the performance improves. Despite the wide range of variability of the yearly heating needs index in the several ages, this result underlines the energy improvement of more recent buildings. This phenomenon gives information about the technical praxis and the market of buildings components. Opaque and transparent envelope surfaces provided by the local market have been drastically improved in the last 20 years (and the same is also for the heating systems). Further, the introduction ® of the Minergie standard9 in Switzerland has been an important driver towards the global energy improvement of new buildings. According to Eqs. (1) and (2), the Qj values were calculated for ®
each building taking into account the EIj (for building in CECE ) or the EI i (the average value for that age) and the EAj (available for each building). These values were than aggregated by age, providing the total heating demand for each age and, as sum, of the global residential building stock.
9 The Minergie standard was developed in 1994 and in 1998 the Minergie Agency officially started working with positive effects on the evolution of the market related to the improvement of the buildings energy performance.
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223
7000000 6000000
30000 25000
5000000
20000
4000000
15000
3000000 2000000
10000
number
heated surfaces m2
Residenal buildings in Canton Ticino, surfaces and n° by age
5000
1000000
0
0 before 1919 1919-1945 1946-1960 1961-1970 1971-1980 1981-1990 1991-2000 2001-2010 aer 2010
Age of construcon n° buildings
heated surfaces m2
Fig. 4. Residential buildings in Canton Ticino, total energy reference surfaces and total number by age.
main datasets: CECE for buildings with energy cerficate; RDB for all the buildings. Building j; Age i CECE database
Data elaboraons
sample of 1,473 cerfied re si de nti al bui l di ngs
108,064 re si de nti al bui l di ngs
data extracted: EI_j of each buil ding
RBD database
data extracted: classificaon of buildings by age
Age of e ach bui l di ng
calculaon of mean EI for e ach age : EI_i
EA_j of e ach bui l di ng
(EI_j x EA_j) of each cerfied residenal buil ding
(EI_i x EA_j) of each NOT cerfied residenal building
calculaon of the heang needs of the residenal buildings in Canton Ticino Fig. 5. Scheme of data collection and elaboration for describing space heating needs in residential buildings of Canton Ticino.
Results are reported in Fig. 7, where the impact of the older buildings (in particular of those built between 1945 and 1970) is evident. 3.1. Verification of the results As previously presented, our method attempts to merge topdown approach with bottom-up approach. Referring to our case of application, the top down approach includes the analysis of the dataset available for Canton Ticino useful for the characterization of the residential stock. In particular we refer to the mentioned RBD [18], the Database of heating plants controls [24] and the subsequently described Cantonal Energy Balance. While the bottom-up approach is referred to the energy audit procedure accordingly to SIA 380/1 [19] and SIA 2031 [20] and aimed at the provision of ® CECE .
To this end, the results obtained in Section 3 were compared to official statistic reports (i.e. the Cantonal Energy Balance) in order to attest the global correctness and effectiveness of the method. In particular, a first comparison regards the buildings fueled by natural gas boilers, since these plants can be precisely monitored due to the meters. In number, natural gas heating systems represent about 1/5 of the small combustion plants and about 1/3 of the big combustion plants in Canton Ticino, as reported in the Database of heating plants controls [24]. The others heating systems are fueled by oil and biomass or consist in heap pumps, other electric systems and heat exchangers connected to district heating. The yearly natural gas consumptions are available in the Cantonal Energy Balance [25]. Actual data are available since the consumptions are measured by the meters and collected by the distribution utilities. Differently, for the other mentioned heating
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Mean heang needs in kWh/m2y by age of construcon 200 180 160
154
149
141 131
140 120
100 100 76
80
60 60 40 16
20
14
0 before 1919 1919-1945 1946-1960 1961-1970 1971-1980 1981-1990 1991-2000 2001-2010 aer 2011 Fig. 6. Evaluation of the average yearly heating needs index by age of construction for the certified residential buildings in Canton Ticino.
Esmaon of the heang demand by age, GWh/y aer 2010 2001-2010 1991-2000 1981-1990 1971-1980 1961-1970 1946-1960 1919-1945 before 1919 0
100
200
300
400
500
600
700
800
900
Fig. 7. Evaluation of the global heating needs for the residential buildings in Canton Ticino.
systems, the monitoring of the energy consumptions are more difficult and error-prone. Unfortunately, the categories in which data are aggregated in the Cantonal Energy Balance [25] do not permit to precisely single out natural gas consumption related only to residential users. This is a typical limit of the application of the top-down approach. For this reason, looking at data on natural gas consumption reported in the Cantonal Energy Balance [25], the following categories were taking into account: heating in homes and dwellings, gas adopted in craft and industry buildings, gas adopted in trade and services buildings. The natural gas consumed in buildings belonging to these three categories in 2015 was 1098 GWh and included data in terms of primary energy consumption for thermal purposes (i.e. space heating and domestic hot water and very few process uses). Therefore, other elaborations, in addition to those described in Section 2, were carried out. In particular, the thermal needs for domestic hot water (DHW) and for the space heating demand of non-residential buildings were estimated accordingly to SIA 380/1 [19]. This comprehensive norm includes not only the method for the calculation of the buildings energy flows, but also typical standards of space heating and DHW needs for several categories of buildings depending on their use as reported in the following Tables 3 and 4.
In fact, SIA 380/1 [19] defines the limit energy needs for providing DHW based on the use and on the energy reference surface of the building, as reported in Table 3 for residential buildings. In the case of DHW, thermal needs do not depend on the energy performance of the envelope, but only on the surfaces and on the geometry (single or multifamily). These results can be added to the space heating needs presented in the previous sections in order to estimate the total thermal energy needs of the residential buildings. SIA 380/1 [19] was adopted also for the estimation of space heating needs in non-residential buildings, i.e. buildings not subject to ® the provision of CECE .10 Data described in official reports permit to identify the type of activities and other information useful to estimate the space heating needs as the product of the energy reference surface reported in RBD [18] and the limit value of space heating needs per surface unit reported in SIA 380/1 [19]. The authors are aware that this estimation may imply a certain level of error compared to the operational data, but this procedure is the only applicable adopting the available statistical information.
®
10 The CECE procedure is possible for residential buildings, administrative buildings and schools. In case of mixed use, the procedure is possible only if the prevalent ® use represents at least the ¾ of the building. The number of CECE related to nonresidential buildings in Ticino is very low and does not permit the application of the same method adopted in Section 2 for residential buildings.
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Table 3 Energy needs for DHW as provided by SIA 380/1 [19].
DHW, energy needs in kWh/m2 y
Single-family residential buildings
Multi-family residential buildings
14
21
Table 4 Thermal needs for non-residential buildings as provided by SIA 380/1 [19]. Category in SIA 380/1
Types
DHW, kWh/m2 y
Space heating, kWh/m2 y
III IV V VI VII VIII IX X XI XII
administrative buildings schools shops restaurants public spaces hospitals industrial buildings warehouses sports buildings swimming pool buildings
7 7 7 56 14 28 7 1 83 83
37 43 32 58 51 40 52 56 68 82
Table 5 Evaluation of the global thermal demand to be satisfied by natural gas boilers. Space heating in residential building, GWh/ya
Space heating in non-residential building, GWh/yb
DHW, GWh/yc
Total, GWh/y
654
119
162
935
a b c
Evaluated following the method presented in section 2 (70% of the total). Evaluated on the basis of the index provided in SIA 380/1 [19], as reported in Table 4 (13% of the total). Evaluated on the basis of the index provided in SIA 380/1 [19], as reported in Tables 3 and 4 (17% of the total).
Further, non-residential buildings represent the lower contribution to heating needs, as reported in Table 5, so the approximation involves in any case a limited error. In order to verify results obtained by the application of our method reported in Section 2.2 and Fig. 5, we were forced to estimate all the thermal needs provided by natural gas fueled plants, since these data are aggregated in the Cantonal Energy Balance. To this end we considered:
• space heating needs for residential buildings (fueled by natural gas) calculated by the method described in Section 2.2; • DHW thermal needs for all the buildings (homes and dwellings, craft and industry, trade and services) fueled by natural gas estimated by standard values reported in SIA 380/1 and energy reference surfaces reported in RBD [18]; • space heating needs for non-residential buildings (craft and industry, trade and services) fueled by natural gas estimated by standard values reported in SIA 380/1 and energy reference surfaces reported in RBD [18].
Referring to year 2015, these values are reported in Table 5 and globally correspond to a global thermal demand of 935 GWh. Further, since the diffusion of natural gas boilers is fairly recent in Ticino, it is reasonable to assume an average yearly thermal efficiency (ratio between heat provided and primary energy consumed as natural gas) of about 90%, as intermediate yearly value considering both condensing and not condensing natural gas boilers. The ratio between the global thermal demand of 935 GWh and average thermal efficiency of natural gas boilers brings a result of primary energy consumption equal to 1038 GWh/y, perfectly in line with the Cantonal Energy Balance [25] that, as mentioned, reports a value of 1098 GWh in 2015. Despite the mentioned limits (only natural gas systems and estimation of DHW and space heating for non-residential buildings according to SIA 380/1), it is possible to argue that this verification confirms the global reliability of the method.
3.2. Application to the evaluation of the energy renovation potential The method developed is able to characterize the energy performance of the built environment at territorial scale. This result represents as a basis to evaluate the effects of energy retrofit strategies. In fact, results reported in the previous sections were adopted in order to define scenarios of energy renovation according to the standard defined by federal and local programs. In Switzerland, it is possible to access a special program of incentives devoted to support energy renovation11 towards the reduction of fossil primary energy and CO2 emissions. In order to obtain these incentives, an advice report has to be provided, able to accurately demonstrate the energy improvements due to the applied measures of energy renovation. Analyzing data referred to buildings renovation, the authors learned that about two third of the residential buildings renovated ® in the last years belong to the CECE energy class G (Fig. 2). They were mostly built during the period from 1946 to 1970. The authors learned also that heating needs reached after the renovation are quite independent from the age, since there are standards defined by laws to be reached. In the previous sections, it was recognized that the most problematic buildings are those built before 1970. In order to define energy renovation scenarios, it is possible to estimate a basic renovation target taking into account that all the residential buildings built before 1970 are retrofitted towards the average yearly heating needs index of the next age (100 kWh/m2 y, as reported in Fig. 6). This measure corresponds to a global saving of 22% in terms of space heating needs. In order to provide a more drastic reduction, it is possible to assume that all the residential buildings built before 2010 are retrofitted towards the energy index currently requested for new buildings (14 kWh/m2 y, as reported in Fig. 6). This measure corresponds to an impressive global saving
11 www.dasgebaeudeprogramm.ch (in Italian, French and German); www.4.ti.ch/ dt/da/spaas/uacer/temi/risparmio-energetico/basi-legali/basi-legali/incentivi/ (in Italian).
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% of reducon in respect to current condion
Reducon of the space heang needs aer global energy renovaon 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% EI ≤ 100 kWh/m2y
EI ≤ 14 kWh/m2y
Fig. 8. Estimation of a hypothetical path of energy renovation for the residential buildings in Canton Ticino.
of 86% in terms of space heating needs. We underline that these results are referred only to the space heating needs estimation in residential buildings and without taking into account DHW needs and systems efficiencies. These results are reported in Fig. 8 where a hypothetical path of energy renovation is drawn. This figure implies the possibility to halve the space heating demand in residential buildings by the application of on the shelf and affordable measures, technics and components. A deep multi-criteria evaluation is needed in order to allow technicians and other involved stakeholders to determine where to approach the intervention according to others specific requirements, as explained also in Lizana et al. [26].
4. Discussion and conclusions In order to be effective, local energy plans and policies have to be supported by a reliable knowledge of the energy performance of the local building stock. Hybrid models, able to merge regional and country-level building stock models (top-down) with Building Energy Models (Bottom-up), represent an interesting contribution to this end. The strength of the method here presented is based on the following two main points: the accuracy and reliability of the Swiss energy certification procedure and the availability of reliable statistic data and norms about buildings features. The first makes ® possible to access the CECE database and to extract the yearly heating needs index for each certified building being sure of the correctness and precision of that index. The second makes possible to access the RBD database and to extract important information such as the energy reference surface and the age of each building. These figures are available and reliable for all the buildings. In other contexts, the evaluation of the buildings energy performance by the energy certification does not have adequate level of correctness. Further very often building stock statistics are scattered among several authorities and are often less reliable and not uniform. Differently, Canton Ticino, such as also the other Swiss Cantons, could boast effective and constantly updated statistic supports and a valid energy certification procedure. This situation underlines the opportunity of improving the method to accomplish properly the energy certification and of reforming and upgrading statistic database and municipal technical offices wherever necessary and possible, in other territories and contexts.
The proper ambit in which the application of the method developed makes sense is the building stock of a territory (like the Canton), or a municipality or, at least, a community. In this framework, the achieved results represent a precious support to the development of local energy plans and energy policies. The main seeming limit of the method developed lies in the ratio between the certified buildings (the sample of buildings taken ® from CECE ) and the total building stock (all the building taken from RBD). Surely, this ratio it is likely to increase in the future due ® to the continuous enrichment of CECE . Further, the significance of the sample buildings is mainly related to the segmentation of the buildings stock, as explained in Reinhart and Cerezo Davila [7]. In this phase of the research, age and use were adopted as segmentation parameters, while other parameters like climate and systems12 were postponed to further developments of the research (e.g. buildings clusters depending on degree-days, location, type of plants etc.) due to the apparent minor importance in relation to the context of Canton Ticino. The method here presented permits a continuous updating. This is a fundamental aspect in order to monitor the evolution of the energy performance of buildings. The trackable decreasing of heating needs during the time (as it is expected) can be considered as an indicator of the improvements of the buildings technologies. As further developments of the method, the authors are engaged in the implementation of a GIS based tool for a territorial representation of the results. GIS could support evaluations and decision making towards energy saving and it is suitable for simulating the effects of the implementation of new standards of building energy performances. This kind of representation makes decision makers able in finding the most effective and affordable (also from the economic and social point of view) energy policies and strategies at the territorial/urban levels. The possibility to operate in a territorial/urban energy context could be the winning strategy in order to drastically reduce energy waste in the building stock and to promote different energy paradigms, such as district energy strategies. This approach represents the premise to create the opportunities to start big projects and to meet low energy and low carbon standards in a more cost effective way using renewable energy and efficient district systems.
12 Systems were considered in the validation phase when only buildings equipped by natural gas boilers were taken into account.
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Contents lists available at ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Evaluation of the space heating need in residential buildings at territorial scale: The case of Canton Ticino (CH) Luca Pampuri a , Nerio Cereghetti a , Pamela Galbani Bianchi a , Paola Caputo b,∗ a Istituto Sostenibilità Applicata All’ambiente Costruito, Scuola Universitaria Professionale della Svizzera Italiana, Campus Trevano, Via Trevano, CH-6952 Canobbio, Switzerland b Department of Architecture, Built Environment and Construction Engineering, Politecnico di Milano, Via Bonardi 9, 20133 Milano, Italy
a r t i c l e
i n f o
Article history: Received 24 October 2016 Received in revised form 13 February 2017 Accepted 21 April 2017 Available online 26 April 2017 Keywords: Heating Residential buildings Energy performance estimation Energy renovation
a b s t r a c t Due to the energy and environmental emergencies, in the last decades, goals, standards, regulations and incentives have been defined according to governmental strategies and regulations at national or local level. Referring to the built environment, this implies a knowledge of the energy performance of the building stock that instead is still poor. A method able to characterize the energy performance of the built environment at territorial scale was developed. It follows a simplified procedure that takes into account: data available in the Energy Certificate of Buildings database, data about the age of the buildings and the energy reference surfaces available in official statistics database. The method was applied to residential buildings in Canton Ticino, in southern Switzerland. The results achieved were compared to information available in the Cantonal Energy Balance, with regards to gas fueled residential buildings. The comparison reveals the global reliability of the method if applied at territorial scale. This method could support energy policies at regional/urban level, in order to stimulate a rational and massive refurbishment of the built environment. Further improvements are in progress in order to provide a territorial representation of results by a Geographic Information Systems (GIS) tool. © 2017 Elsevier B.V. All rights reserved.
1. Introduction The path to carbon neutral communities represent long lasting and urgent challenges for all governments. For example, the EU has approved the “Roadmap for moving to a competitive low carbon economy in 2050” [1] with the objective to reduce greenhouse gas emissions by 80–95% by 2050 in comparison to those of 1990. Analogously Switzerland has approved the Energy Strategy 2050 that includes the main aims for the energy policy towards 2050 [2]. Energy statistics show that European and Swiss buildings are responsible of more than the 40% of the total final energy consumption [2,3], representing an important energy saving opportunity. More in details, energy statistics show that buildings space heating accounts for 68% and 71% of end-use energy consumption respectively in Europe and Switzerland, while lighting and electrical appliances account for 15%, water heating for 12% and cooking for 4% [2,3]. These information underline the importance to oper-
∗ Corresponding author. E-mail address: [email protected] (P. Caputo). http://dx.doi.org/10.1016/j.enbuild.2017.04.061 0378-7788/© 2017 Elsevier B.V. All rights reserved.
ate on the thermal final uses in buildings, with particular regard to those built during times when the requirements on energy performance were much less restrictive. Previous researches [4–6,23] stressed the importance to know which buildings mainly affect energy consumption and environmental problems and to investigate the effects that energyretrofitting programs may have in the future. These researches have explained that the analysis of the energy flows in buildings can be described by top-down building stock energy models or bottom-up models. The first models study energy flows among a defined territory and then deepen the analysis of smaller parts of the existing building stock. Reinhart and Cerezo Davila [7] explained that these models are suitable for analyzing the global energy flows of a large building stock (e.g. energy consumptions by fuel or by sector for a defined territory) and for developing and simulating future scenarios. Conversely, these models are less suitable for focusing the evaluations on a specific neighborhood or for investigating detailed figures (e.g. types of final energies and their distribution among a territory). The bottom-up approach adopts input data from a lower level (such as individual or groups of buildings) and extrapolates the results for the whole sector according to the representative
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weight of the sample considered. Reinhart and Cerezo Davila [7] indicated that “bottom-up urban building energy models (UBEMs)” are more suitable at neighborhood scale, ranging from several dozen to thousands of buildings. They also stressed the features of “building energy models (BEMs)” at the individual building level and the limit related to the discrepancies between simulated and measured energy consumptions, that are mainly due to the uncertainty related to infiltration rates, equipment loads and occupants behavior. The scientific community recognizes that in the last years several BEMs have been developed and refined. They have become increasingly precise and accurate, often equipped by a user-friendly interface. However, their use requires a good knowledge of buildings physics; detailed information about envelope, systems and plants of the buildings; sufficient time to input data and to verify results; and, consequently, a consistent budget to activate these competences. Therefore, these methods and tools are applicable to small group of existing buildings, so their application cannot be considered as a pivotal support in energy planning and energy policies definition. Therefore, we can state that large building stock evaluations still represent a challenging topic and are currently covered by many studies aimed at investigating energy consumption mainly in the residential buildings sector. In addition to the interesting insights about how to synthetize building load profiles by UBEMs reported in Reinhart and Cerezo Davila [7], we can mention also the researches carried out by Swan and Ugursal [8], Theodoridou and Papadopoulos [9], Fracastoro and Serraino [10], Dall’O’ et al. [11], Ma et al. [12], Zhao and Magoulès [13], Hamilton et al. [14], Mata et al. [15], Mirakyan and De Guio [16], Ballarini et al. [17] and Caputo and Pasetti [6]. Many of these contributions suggest abstracting a building stock into building archetypes, an approach suitable for large-scale bottom-up building energy models. As reported in Reinhart and Cerezo Davila [7] and in Ballarini et al. [17], the definition of an archetype can be either based on a sample building (an actual building within the group that is documented by an audit or another detailed energy report) or on a virtual building which is based on statistical building data and/or expert opinion. The research here presented follows the same approach, where the sample buildings are those registered in the Cantonal Energy ® Certificate of Buildings (CECE ). Based on the method described in Section 2, the research investigates the heating needs in a large building stock (i.e. the residential buildings of Canton Ticino); demonstrates the reliability of the achieved results; and provides a first estimation of the benefits due to energy renovation measures. This last point represents a first contribution, to be faced more systematically in the further developments of the research, for defining energy retrofit scenarios able to support the development of massive and effective measures and policies.
1.1. Context: the case of Canton Ticino The context of application is the territory of Canton Ticino, a region of about 2800 km2 and 350,000 people in southern Switzerland (Fig. 1). Due to geographic position, in Canton Ticino various landscapes, morphologic and climatic conditions are present, so it can be consider a sample of different European regions (like northern Italy,1 southern Germany, part of Austria and France). Referring to winter conditions, the standard Degree-Days (DD) in Ticino range from 2326 in Lugano station to 5287 in the moun-
1 Not by chance, Insubria is a defined region that includes the Swiss Canton Ticino and parts of the Italian regions of Lombardy and Piedmont.
219
Fig. 1. Location of the territory under examination in the European context.
tain station of San Bernardino.2 Low elevation areas like Lugano has a temperate/continental climate, similar to the adjacent northern Italy, while high elevation zone have alpine climatic conditions. Further, due to history and level of development, the building stock of Canton Ticino presents architectural technologies, types of envelope and ages similar to those averagely present in Europe. For example, Canton Ticino has 125 people per km2 , little more than the EU average (113 people per km2 ) and quite similar to the Italian average (200 people per km2 ). If, as a collation, we compare residential buildings in Ticino to residential buildings in Italy, we find many similitudes. In fact, in both the cases, the most part of residential buildings are small and stand alone, about 2/3 of the buildings has one or two floors (69%3 and 67%4 in Swiss and Italy, respectively) and the most part is constructed by load-bearing wall. Furthermore, residential buildings built after 1990 represent respectively the 11% in Italy and the 16% in Swiss, evident signal of a great need of renovation. Analogous comparison can be reported also considering other adjoining European countries, such as Germany, France, Austria etc. However, unlike other European countries, Ticino (and other Cantons of Swiss) was less affected by the impact of the Second World War and of the socio-economic boom of the ‘60 s and ‘70 s on its building stock. In fact, in Ticino, residential buildings built before 1961 represent the 56% of the total (e.g. in Italy they are the 44%), while residential buildings built between 1961 and 1990 represent the 29% of the total (e.g. in Italy they are the 45%). Details about types of residential buildings and related ages in Ticino are reported in Sections 2.1 and 2.2. 2. Method In section 1, we concluded that a reliable estimate of heating needs in existing residential buildings is a key requirement in energy planning at territorial scale and to define effective energy policies. We underlined that top-down methods do not permit to achieve this knowledge and, on the other hand, bottom-up method in general provide trusted energy information only for small group of buildings. This knowledge building boosts researchers toward exploring new hybrid methodologies, such as that developed in the framework of the present research. More precisely, our aim is to develop a method for the characterization of the energy performance in terms of heating needs of a large building stock. This method is expected to be: simple, transparent, based on data avail-
2 Average DD based on monthly data of the last 30 years provided by the statistical office of Canton Ticino. 3 Data reported in RBD [18]. 4 Data reported in the Italian census (www.istat.it).
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able in official dataset and statistics, replicable, upgradable over time and compatible with GIS supported tools. The method integrates a bottom-up approach because of the selection of the sample buildings and of the analysis of their energy audits. Nevertheless, the method integrates also a top-down approach because of the investigation of the statistics information about the features of the entire building stock (e.g. use, age, energy reference surface, number of floors, address etc.) and because of the verification of the results by the comparison to global data available in the Cantonal Energy Balance, as reported in Section 3.1. 2.1. Available datasets In Swiss, the main information about the built environment are collected in the Federal Register of Buildings and Dwellings [18], a very rich, verified and updated dataset. In connection with the 2000 Population Census,5 the Federal RBD was created on the basis of the Housing Census carried out at the same time. The Federal RBD includes all buildings in Switzerland used for residential purposes and the dwellings therein. In addition to unambiguous and nationwide-unique building and dwelling identification codes, the Federal RBD records the most important basic data, such as address, location details, year of construction, number of floors, type of heating for the buildings, and for the dwellings, number of rooms, size (floor area), etc. The Federal Statistical Office (FSO) administers the Federal RBD in close cooperation with municipal construction authorities as well as with specialized bodies in the federal government, cantons and municipalities. The construction authorities notify the FSO of all building projects subject to construction approval (new buildings, conversions or renovations, demolitions) via the web, using designated interfaces or questionnaires. RBD [18] was deeply investigated with particular regard to the age and the energy reference surface of each residential building in Canton Ticino. RBD [18] classifies the age of construction of each building as follows: 1. 2. 3. 4. 5. 6. 7. 8. 9. 10. 11. 12. 13. 14. 15.
before 1919; from 1919 to 1945; from 1946 to 1960; from 1961 to 1970; from 1971 to 1980; from 1981 to 1985; from 1986 to 1990; from 1981 to 1985; from 1986 to 1990; from 1991 to 1995; from 1996 to 2000; from 2001 to 2005; from 2006 to 2010; from 2011 to 2015; after 2015.
On the basis of statistical evaluations of the architecture technologies related to the different ages, the classes listed above were reduced to 9 age classes including, at least, a range of 10 years, as reported in the following Table 2. Since the RBD does not include the information about the energy needs of the buildings, another dataset was explored. Since the ® institution of the Cantonal Energy Certificate of Buildings (CECE ) in 2009, in Swiss a uniform method for the evaluation of the buildings energy performance was provided accordingly to the federal
norms. This method is aimed to support energy evaluations, both at the envelope level and at the building level (envelope, systems, ® plants, conditions of operation). Currently CECE is voluntary and is suitable for residential buildings, simple administrative buildings and schools. If a building has a mixed use (i.e. residential and commercial), it is possible to apply the CECE only if the residential part is more than ¾ of the total. ® The tool for the elaboration of CECE were developed by the ® 6 support of the MINERGIE Agency. The calculation is based on a steady state method based on monthly data, according to SIA7 380/1 [19] and SIA 2031 [20]. Climatic data for the evaluation are defined according to SIA 2028 [21], referring to the period from 1984 to 2003. ® In addition to CECE , it is possible to provide also a detailed ® advice report, called CECE Plus. This is a detailed energy report provided by an energy analyst. In details, the report includes a set of customized measures for improving the buildings energy efficiency, the technical, economic and financial details of the possible interventions and an expert support in realizing the building renovation. ® ® CECE and CECE Plus can be required by the owner or by the administrator of the building and can be compiled and pro® vided only by CECE certified experts. In order to complete the certification, they carry out an on-site inspection and an accurate examination of the building. The strict relationship between the owner of the building (that has to provide data about energy consumption, i.e. space heating, domestic hot water and electricity, at least for the last three years) and the energy expert (that starts the analysis with an on-site survey) is a pivotal point of the pro® cedure. In general, the CECE implies at least 5–10 working hours ® and a cost of at least 500 CHF, while the CECE Plus implies at least 15–25 working hours and a cost of at least 1500 CHF. ® The CECE report provides the energy certification of the building by the energy label. This is based on the calculated buildings energy needs that are then compared to actual energy consumption. After the calculation of the energy performance, it is possible to classify the buildings in 7 classes from A (the most efficient) to G (the least efficient). Table 1 reports the classification considering only the envelope performances. ® The classification provided by CECE is based on the contents of SIA 2031 [20]. In brief, for each building a limit value of the heating needs is calculated. This value is compared to the actual value of the analyzed building. The limit value corresponds to the interface between class B and C (100% of the limit), while the other classes are set by increasing or decreasing of 50% as reported in Fig. 2. Buildings with heating needs higher than 300% of the limit value are in class G. For example, if a building has a heating need equal to 130 kWh/m2 y and the limit value for that building (taking into account its climatic condition, geometry etc.) is 100 kWh/m2 y, the class is C, since 130/100 is included between 100% and 150% of the limit value. This means that the energy classes are defined building by building and there are not fixed range of heating needs for identifying the classes. A similar approach was adopted also in other energy certification procedures, such as that in force in Lombardy Region in northern Italy.8 ® The residential buildings of Canton Ticino registered in CECE represent a wide sample of buildings for which a reliable energy analysis is available and accessible, since the procedure briefly described imply a detailed energy audit, differently from other
6
www.minergie.ch (in Italian, French and German). SIA is the Swiss Society of Engineers and Architects (www.sia.ch). Among its aims, SIA provides norms and regulations about energy and buildings issues. 8 www.cened.it (in Italian). 7
5
www.bfs.admin.ch.
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Table 1 ® Energy classification at envelope level as provided by CECE (www.cece.ch).
Table 2 ® Number of residential buildings by age and by energy class (CECE database, update at end 2015). Energy class Age 1 2 3 4 5 6 7 8 9
A
Before 1919 1919–1945 1946–1960 1961–1970 1971–1980 1981–1990 1991–2000 2001–2010 After 2011 Total
2 1
2 34 39
B
C
D
E
F
G
total per age
total in classes E-F-G, %
13 6 10 16 14 4 1 3 11 78
11 4 14 10 24 49 10 5 2 129
16 8 6 13 34 95 11 2
24 5 14 35 47 81 6
26 11 33 45 51 22 4
122 70 142 177 111 12 4
185
212
192
638
212 104 221 296 282 263 36 12 47 1,473
81% 83% 86% 87% 74% 44% 39% 0% 0% 71%
Fig. 2. Classes of the Swiss energy classification.
types of databases of buildings energy classification. In other cases, in fact, lacks in uniformity, completeness and reliability of the energy certification makes this approach too susceptible to errors. For example, in the context of northern Italy, Dall’O’ et al. [22] provide a selection of the available energy certifications and a definition of a set of representative buildings in order to evaluate the energy performance of a defined building stock. In this case, the problem of the reliability of the available certificates was well stressed, evaluating the risk of managing wrong information or overestimated heating needs. In the same context, Khayatian et al. [23] underline the problem of defective input data in the energy cadaster of buildings in Lombardy, offer a method for detecting
anomalies in buildings energy certificates and try to define a correlation between quasi-steady-state and dynamic simulations. In our case, the certifications referred to buildings in progress were discarded because for these buildings it is not possible to carry out the final verification of the certification during the operation stage, ones completed and occupied. Cleared these considerations, in the present case, a sample of ® 1473 buildings extracted from CECE was taken into account, as described in Table 2. This sample includes single-family and multifamily buildings, located in all the climatic areas of Canton Ticino and represent the 1.4% of the overall residential buildings in the same Canton (108,064 residential buildings as reported in the Federal RBD at end 2015). The first results of the investigation are reported in Fig. 3 and Table 2. It is possible to observe that the most part of certified buildings belongs to ages from 1946 to 1990 that are also the ages with the lower energy performance (i.e. the highest yearly heating needs indexes). The last column of Table 2 reports, for each age, the percentage of buildings belonging to class E, F and G (i.e. that consume the double or more in respect to the reference building). On average the 71% of the total belongs to these classes and this percentage becomes less than the half only for buildings built after 1980. In our method, these buildings represent the sample buildings to be adopted as archetypes accordingly to Reinhart and Cerezo Davila [7]. In fact, in their building archetype definition, Reinhart and Cerezo Davila [7] report many studies in which type of use and age were taken into account in the so called segmentation phase (the phase of the selection of sample buildings able to represent the entire building stock). They also state that each sample building can represent less than 50 up to 500,000 buildings. The considerations mentioned in Section 1 allow us to argue that sample buildings
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Fig. 3. Distribution of the residential buildings in Canton Ticino (total in dark and certified in light) per ages, in percentage. ®
extracted from CECE can be considered as representative of the entire residential building stock, accordingly also to the verification reported in Section 3.1. All these buildings were grouped by age, following the age classification reported in Section 2.2, and the yearly heating needs index (in kWh/m2 y) of each was selected among the available data.
2.2. Estimation of the heating needs In Section 2.1 we concluded with the following selection of information useful in order to apply our method: the energy reference surface of each residential buildings (108,064 as extracted from RBD, 2015) grouped by age; the yearly heating needs index (in kWh/m2 y) of each residential buildings (1473 as extracted from ® CECE database, update at end 2015) grouped by age. Considering the overall residential building stock of Canton Ticino, it is possible to observe as the most part of the buildings were built between 1919 and 1960 (Fig. 4), attesting the old age of the stock and therefore the need of renovation. Further Fig. 4 underlines the low density of the building stock, providing an average ratio between energy reference and number of buildings of about 331 m2 /building. If a building (subscript j) has an energy certificate, it belongs both to the first and to the second dataset. Having the same identification code in both the datasets, we can estimate the space heating needs (Qj in kWh/y) of the j building as follows: Qj = EAj × EIj
(1)
where EAj is the energy reference surface of the j residential building in m2 and EIj is its yearly heating needs index in kWh/m2 y. ®
We stressed that the EI is available by the CECE database only for the certified buildings. Conversely, for the not yet certified buildings the method here presented provide an estimation of the EI based on the age of the building. In fact, the second step of the method provides the calculation of the average value of the EI for each class of age as follows: ®
1. The buildings certified by CECE are grouped by age (age i, i from 1 to 9, according to the classes of age listed in Section 2.1);
2. For each age i, average values of the EI are then calculated on the basis of the information available for the buildings certified by ® CECE following the equation: EI i =
j,i
EIj,i × EAj,i ⁄
j,i
EAj,i
(2)
where EIj,i is the yearly heating needs index of the j residential building belonging to the age i and EAj,i is the energy reference surface of the j residential building belonging to the age i; 3. The EI of the not certified buildings was estimates taking into account their age of construction and assigning the average EI calculated by Eq. (2). The scheme of the method (data collection and elaboration) is shown in Fig. 5. 3. Results On the basis of the described method, it was possible to associate an average yearly heating needs index, EI i , as reported in Fig. 6. The trend-line (based on the yearly heating needs index of certified buildings) underlines a progressive decreasing of heating needs with the age: the more the building is recent, the most the performance improves. Despite the wide range of variability of the yearly heating needs index in the several ages, this result underlines the energy improvement of more recent buildings. This phenomenon gives information about the technical praxis and the market of buildings components. Opaque and transparent envelope surfaces provided by the local market have been drastically improved in the last 20 years (and the same is also for the heating systems). Further, the introduction ® of the Minergie standard9 in Switzerland has been an important driver towards the global energy improvement of new buildings. According to Eqs. (1) and (2), the Qj values were calculated for ®
each building taking into account the EIj (for building in CECE ) or the EI i (the average value for that age) and the EAj (available for each building). These values were than aggregated by age, providing the total heating demand for each age and, as sum, of the global residential building stock.
9 The Minergie standard was developed in 1994 and in 1998 the Minergie Agency officially started working with positive effects on the evolution of the market related to the improvement of the buildings energy performance.
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223
7000000 6000000
30000 25000
5000000
20000
4000000
15000
3000000 2000000
10000
number
heated surfaces m2
Residenal buildings in Canton Ticino, surfaces and n° by age
5000
1000000
0
0 before 1919 1919-1945 1946-1960 1961-1970 1971-1980 1981-1990 1991-2000 2001-2010 aer 2010
Age of construcon n° buildings
heated surfaces m2
Fig. 4. Residential buildings in Canton Ticino, total energy reference surfaces and total number by age.
main datasets: CECE for buildings with energy cerficate; RDB for all the buildings. Building j; Age i CECE database
Data elaboraons
sample of 1,473 cerfied re si de nti al bui l di ngs
108,064 re si de nti al bui l di ngs
data extracted: EI_j of each buil ding
RBD database
data extracted: classificaon of buildings by age
Age of e ach bui l di ng
calculaon of mean EI for e ach age : EI_i
EA_j of e ach bui l di ng
(EI_j x EA_j) of each cerfied residenal buil ding
(EI_i x EA_j) of each NOT cerfied residenal building
calculaon of the heang needs of the residenal buildings in Canton Ticino Fig. 5. Scheme of data collection and elaboration for describing space heating needs in residential buildings of Canton Ticino.
Results are reported in Fig. 7, where the impact of the older buildings (in particular of those built between 1945 and 1970) is evident. 3.1. Verification of the results As previously presented, our method attempts to merge topdown approach with bottom-up approach. Referring to our case of application, the top down approach includes the analysis of the dataset available for Canton Ticino useful for the characterization of the residential stock. In particular we refer to the mentioned RBD [18], the Database of heating plants controls [24] and the subsequently described Cantonal Energy Balance. While the bottom-up approach is referred to the energy audit procedure accordingly to SIA 380/1 [19] and SIA 2031 [20] and aimed at the provision of ® CECE .
To this end, the results obtained in Section 3 were compared to official statistic reports (i.e. the Cantonal Energy Balance) in order to attest the global correctness and effectiveness of the method. In particular, a first comparison regards the buildings fueled by natural gas boilers, since these plants can be precisely monitored due to the meters. In number, natural gas heating systems represent about 1/5 of the small combustion plants and about 1/3 of the big combustion plants in Canton Ticino, as reported in the Database of heating plants controls [24]. The others heating systems are fueled by oil and biomass or consist in heap pumps, other electric systems and heat exchangers connected to district heating. The yearly natural gas consumptions are available in the Cantonal Energy Balance [25]. Actual data are available since the consumptions are measured by the meters and collected by the distribution utilities. Differently, for the other mentioned heating
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Mean heang needs in kWh/m2y by age of construcon 200 180 160
154
149
141 131
140 120
100 100 76
80
60 60 40 16
20
14
0 before 1919 1919-1945 1946-1960 1961-1970 1971-1980 1981-1990 1991-2000 2001-2010 aer 2011 Fig. 6. Evaluation of the average yearly heating needs index by age of construction for the certified residential buildings in Canton Ticino.
Esmaon of the heang demand by age, GWh/y aer 2010 2001-2010 1991-2000 1981-1990 1971-1980 1961-1970 1946-1960 1919-1945 before 1919 0
100
200
300
400
500
600
700
800
900
Fig. 7. Evaluation of the global heating needs for the residential buildings in Canton Ticino.
systems, the monitoring of the energy consumptions are more difficult and error-prone. Unfortunately, the categories in which data are aggregated in the Cantonal Energy Balance [25] do not permit to precisely single out natural gas consumption related only to residential users. This is a typical limit of the application of the top-down approach. For this reason, looking at data on natural gas consumption reported in the Cantonal Energy Balance [25], the following categories were taking into account: heating in homes and dwellings, gas adopted in craft and industry buildings, gas adopted in trade and services buildings. The natural gas consumed in buildings belonging to these three categories in 2015 was 1098 GWh and included data in terms of primary energy consumption for thermal purposes (i.e. space heating and domestic hot water and very few process uses). Therefore, other elaborations, in addition to those described in Section 2, were carried out. In particular, the thermal needs for domestic hot water (DHW) and for the space heating demand of non-residential buildings were estimated accordingly to SIA 380/1 [19]. This comprehensive norm includes not only the method for the calculation of the buildings energy flows, but also typical standards of space heating and DHW needs for several categories of buildings depending on their use as reported in the following Tables 3 and 4.
In fact, SIA 380/1 [19] defines the limit energy needs for providing DHW based on the use and on the energy reference surface of the building, as reported in Table 3 for residential buildings. In the case of DHW, thermal needs do not depend on the energy performance of the envelope, but only on the surfaces and on the geometry (single or multifamily). These results can be added to the space heating needs presented in the previous sections in order to estimate the total thermal energy needs of the residential buildings. SIA 380/1 [19] was adopted also for the estimation of space heating needs in non-residential buildings, i.e. buildings not subject to ® the provision of CECE .10 Data described in official reports permit to identify the type of activities and other information useful to estimate the space heating needs as the product of the energy reference surface reported in RBD [18] and the limit value of space heating needs per surface unit reported in SIA 380/1 [19]. The authors are aware that this estimation may imply a certain level of error compared to the operational data, but this procedure is the only applicable adopting the available statistical information.
®
10 The CECE procedure is possible for residential buildings, administrative buildings and schools. In case of mixed use, the procedure is possible only if the prevalent ® use represents at least the ¾ of the building. The number of CECE related to nonresidential buildings in Ticino is very low and does not permit the application of the same method adopted in Section 2 for residential buildings.
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Table 3 Energy needs for DHW as provided by SIA 380/1 [19].
DHW, energy needs in kWh/m2 y
Single-family residential buildings
Multi-family residential buildings
14
21
Table 4 Thermal needs for non-residential buildings as provided by SIA 380/1 [19]. Category in SIA 380/1
Types
DHW, kWh/m2 y
Space heating, kWh/m2 y
III IV V VI VII VIII IX X XI XII
administrative buildings schools shops restaurants public spaces hospitals industrial buildings warehouses sports buildings swimming pool buildings
7 7 7 56 14 28 7 1 83 83
37 43 32 58 51 40 52 56 68 82
Table 5 Evaluation of the global thermal demand to be satisfied by natural gas boilers. Space heating in residential building, GWh/ya
Space heating in non-residential building, GWh/yb
DHW, GWh/yc
Total, GWh/y
654
119
162
935
a b c
Evaluated following the method presented in section 2 (70% of the total). Evaluated on the basis of the index provided in SIA 380/1 [19], as reported in Table 4 (13% of the total). Evaluated on the basis of the index provided in SIA 380/1 [19], as reported in Tables 3 and 4 (17% of the total).
Further, non-residential buildings represent the lower contribution to heating needs, as reported in Table 5, so the approximation involves in any case a limited error. In order to verify results obtained by the application of our method reported in Section 2.2 and Fig. 5, we were forced to estimate all the thermal needs provided by natural gas fueled plants, since these data are aggregated in the Cantonal Energy Balance. To this end we considered:
• space heating needs for residential buildings (fueled by natural gas) calculated by the method described in Section 2.2; • DHW thermal needs for all the buildings (homes and dwellings, craft and industry, trade and services) fueled by natural gas estimated by standard values reported in SIA 380/1 and energy reference surfaces reported in RBD [18]; • space heating needs for non-residential buildings (craft and industry, trade and services) fueled by natural gas estimated by standard values reported in SIA 380/1 and energy reference surfaces reported in RBD [18].
Referring to year 2015, these values are reported in Table 5 and globally correspond to a global thermal demand of 935 GWh. Further, since the diffusion of natural gas boilers is fairly recent in Ticino, it is reasonable to assume an average yearly thermal efficiency (ratio between heat provided and primary energy consumed as natural gas) of about 90%, as intermediate yearly value considering both condensing and not condensing natural gas boilers. The ratio between the global thermal demand of 935 GWh and average thermal efficiency of natural gas boilers brings a result of primary energy consumption equal to 1038 GWh/y, perfectly in line with the Cantonal Energy Balance [25] that, as mentioned, reports a value of 1098 GWh in 2015. Despite the mentioned limits (only natural gas systems and estimation of DHW and space heating for non-residential buildings according to SIA 380/1), it is possible to argue that this verification confirms the global reliability of the method.
3.2. Application to the evaluation of the energy renovation potential The method developed is able to characterize the energy performance of the built environment at territorial scale. This result represents as a basis to evaluate the effects of energy retrofit strategies. In fact, results reported in the previous sections were adopted in order to define scenarios of energy renovation according to the standard defined by federal and local programs. In Switzerland, it is possible to access a special program of incentives devoted to support energy renovation11 towards the reduction of fossil primary energy and CO2 emissions. In order to obtain these incentives, an advice report has to be provided, able to accurately demonstrate the energy improvements due to the applied measures of energy renovation. Analyzing data referred to buildings renovation, the authors learned that about two third of the residential buildings renovated ® in the last years belong to the CECE energy class G (Fig. 2). They were mostly built during the period from 1946 to 1970. The authors learned also that heating needs reached after the renovation are quite independent from the age, since there are standards defined by laws to be reached. In the previous sections, it was recognized that the most problematic buildings are those built before 1970. In order to define energy renovation scenarios, it is possible to estimate a basic renovation target taking into account that all the residential buildings built before 1970 are retrofitted towards the average yearly heating needs index of the next age (100 kWh/m2 y, as reported in Fig. 6). This measure corresponds to a global saving of 22% in terms of space heating needs. In order to provide a more drastic reduction, it is possible to assume that all the residential buildings built before 2010 are retrofitted towards the energy index currently requested for new buildings (14 kWh/m2 y, as reported in Fig. 6). This measure corresponds to an impressive global saving
11 www.dasgebaeudeprogramm.ch (in Italian, French and German); www.4.ti.ch/ dt/da/spaas/uacer/temi/risparmio-energetico/basi-legali/basi-legali/incentivi/ (in Italian).
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% of reducon in respect to current condion
Reducon of the space heang needs aer global energy renovaon 100% 90% 80% 70% 60% 50% 40% 30% 20% 10% 0% EI ≤ 100 kWh/m2y
EI ≤ 14 kWh/m2y
Fig. 8. Estimation of a hypothetical path of energy renovation for the residential buildings in Canton Ticino.
of 86% in terms of space heating needs. We underline that these results are referred only to the space heating needs estimation in residential buildings and without taking into account DHW needs and systems efficiencies. These results are reported in Fig. 8 where a hypothetical path of energy renovation is drawn. This figure implies the possibility to halve the space heating demand in residential buildings by the application of on the shelf and affordable measures, technics and components. A deep multi-criteria evaluation is needed in order to allow technicians and other involved stakeholders to determine where to approach the intervention according to others specific requirements, as explained also in Lizana et al. [26].
4. Discussion and conclusions In order to be effective, local energy plans and policies have to be supported by a reliable knowledge of the energy performance of the local building stock. Hybrid models, able to merge regional and country-level building stock models (top-down) with Building Energy Models (Bottom-up), represent an interesting contribution to this end. The strength of the method here presented is based on the following two main points: the accuracy and reliability of the Swiss energy certification procedure and the availability of reliable statistic data and norms about buildings features. The first makes ® possible to access the CECE database and to extract the yearly heating needs index for each certified building being sure of the correctness and precision of that index. The second makes possible to access the RBD database and to extract important information such as the energy reference surface and the age of each building. These figures are available and reliable for all the buildings. In other contexts, the evaluation of the buildings energy performance by the energy certification does not have adequate level of correctness. Further very often building stock statistics are scattered among several authorities and are often less reliable and not uniform. Differently, Canton Ticino, such as also the other Swiss Cantons, could boast effective and constantly updated statistic supports and a valid energy certification procedure. This situation underlines the opportunity of improving the method to accomplish properly the energy certification and of reforming and upgrading statistic database and municipal technical offices wherever necessary and possible, in other territories and contexts.
The proper ambit in which the application of the method developed makes sense is the building stock of a territory (like the Canton), or a municipality or, at least, a community. In this framework, the achieved results represent a precious support to the development of local energy plans and energy policies. The main seeming limit of the method developed lies in the ratio between the certified buildings (the sample of buildings taken ® from CECE ) and the total building stock (all the building taken from RBD). Surely, this ratio it is likely to increase in the future due ® to the continuous enrichment of CECE . Further, the significance of the sample buildings is mainly related to the segmentation of the buildings stock, as explained in Reinhart and Cerezo Davila [7]. In this phase of the research, age and use were adopted as segmentation parameters, while other parameters like climate and systems12 were postponed to further developments of the research (e.g. buildings clusters depending on degree-days, location, type of plants etc.) due to the apparent minor importance in relation to the context of Canton Ticino. The method here presented permits a continuous updating. This is a fundamental aspect in order to monitor the evolution of the energy performance of buildings. The trackable decreasing of heating needs during the time (as it is expected) can be considered as an indicator of the improvements of the buildings technologies. As further developments of the method, the authors are engaged in the implementation of a GIS based tool for a territorial representation of the results. GIS could support evaluations and decision making towards energy saving and it is suitable for simulating the effects of the implementation of new standards of building energy performances. This kind of representation makes decision makers able in finding the most effective and affordable (also from the economic and social point of view) energy policies and strategies at the territorial/urban levels. The possibility to operate in a territorial/urban energy context could be the winning strategy in order to drastically reduce energy waste in the building stock and to promote different energy paradigms, such as district energy strategies. This approach represents the premise to create the opportunities to start big projects and to meet low energy and low carbon standards in a more cost effective way using renewable energy and efficient district systems.
12 Systems were considered in the validation phase when only buildings equipped by natural gas boilers were taken into account.
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