ASHRAE OR-16-C071-2016 Bottom-up Assessment of Hellenic Residential Building Stock Energy Performance.pdf

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1、 C.A. Balaras is a mechanical engineer, research director, E.G. Dascalaki is an energy physicist, senior research scientist, Popi Droutsa is a physicist, research assistant and S. Kontoyiannidis is a physicist, research assistant in the Institute for Environmental Research and Sustainable Developmen

2、t (IERSD), National Observatory of Athens (NOA), Athens, Greece. Bottom-up Assessment of Hellenic Residential Building Stock Energy Performance Constantinos A. Balaras, PhD, PEng Elena G. Dascalaki, PhD Fellow ASHRAE Member ASHRAE Popi Droutsa Simon Kontoyiannidis ABSTRACT Within a European research

3、 project there is an ongoing multinational effort to develop a conceptual framework for monitoring the effectiveness of energy efficiency measures (EEMs) applied in European residential buildings. The paper provides an overview of the efforts and outlines the results from a pilot action in Greece fo

4、r identifying the most popular EEMs for residential buildings, the differences of estimated and real energy savings from EEMs and derives adaptation coefficients to support a bottom-up assessment of Hellenic residential building stock energy performance. The work exploits the national energy perform

5、ance certificates data, complemented by a field study to collect evidence of actual energy consumption data from residential buildings before and after EEMs, and behavioral surveys of occupants. The average ratio of actual to calculated primary energy use range from 10% lower for single dwellings to

6、 42% higher for multifamily dwellings. Popular EEMs include building envelope refurbishment (e.g. installing double glazed windows, adding thermal insulation) and upgrading mechanical installations (e.g. replacing an oil-fired boiler with natural gas or a central heat pump, replacing a burner, insta

7、lling a solar collector). Findings reveal that the average source energy savings average 17%, while specific EEM savings can even reach up to 43%. INTRODUCTION The residential building sector is responsible for about 26% of total energy consumption in the European Union (EU) and account for 75% of t

8、he total building stock. About 64% of the residential buildings were constructed before the 80s and the widespread adoption of energy efficiency regulations. From an energy performance point-of-view, this constitutes a grim reality and clearly implies that the majority of European buildings will nee

9、d some kind of refurbishment to the thermal envelope and the electromechanical (E/M) installations to meet the new energy efficiency standards for buildings according to the Energy Performance Directive (EPDB Directive 2010/31/EC). Implemented throughout Europe, EPBD mandates that all buildings that

10、 are subject to major refurbishment should meet minimum energy performance requirements and for new construction as of January 2021 to be nearly zero energy buildings (NZEB). These efforts can play an important role in meeting the European and national targets to become a highly energy-efficient, lo

11、w carbon economy, reduce energy import dependency and increase Europes security of supply in accordance to the European 2020 Strategy (e.g. reduction of greenhouse gas emissions by at least 20% below 1990 levels and a 20% reduction in primary energy use by improving energy efficiency) and the new pl

12、an towards 2030 and beyond. At the same time, the Energy Efficiency Directive (EED Directive 2012/27/EU) brings forward measures to expedite improved energy efficiency at all stages of the full energy chain, including a long-term national strategy for building renovation. The specific targets aim to

13、 achieve by 2016 an overall national indicative energy savings of 9% compared with the average final energy consumption for the five-year period of 2001-2005. This is to be reached by way of energy services and other cost-effective, practicable and reasonable energy efficiency measures (EEMs). Most

14、EU national energy efficiency action plans focus on public buildings and residential buildings. In Greece, the final (source) energy use in Hellenic buildings was 7.27 million tons of oil equivalent (Mtoe) or about 42.4% of the total final energy consumption in 2012 (EU 2014). Residential buildings

15、consume over half of the electricity and over 90% of the thermal energy required by the Hellenic building sector, reaching 5.04 Mtoe or 29.4% of the total final energy use in 2012. Given the economic recession in several European countries, including a major financial crisis in Greece, the efforts f

16、or reducing energy operational costs in dwellings (e.g. for space heating) result to poor or even unacceptable indoor thermal conditions in millions of residential buildings. Available data (ELSTAT 2014) clearly indicate that fuel poverty is a grim reality for 29.4% of the Hellenic population, with

17、48.6% of the poor population and 24.3% of the non-poor population being unable to keep their home adequately warm. In general, EEMs are considered as the most sustainable solution to combat fuel poverty. National EPBD transposition was enacted in 2010 (Dascalaki et al. 2012) by the Hellenic regulati

18、on on the energy performance in the building sector (KENAK). Regardless of codes and regulations, more energy efficient buildings may provide better living conditions and lower energy bills. EEMs for the refurbishment of inefficient buildings is a logical path forward in order to extend the useable

19、lifespan and functions of the existing building stock, while preserving their architectural and cultural heritage. Overall, there are various approaches used to handle energy use performance and environmental impact for building stock models, which are mainly identified as top-down and bottom-up (Ka

20、vgic et al. 2010). The top-down approach aims at fitting a historical time series of national energy use or CO2 emissions to investigate the inter-relationships between the energy sector and the economy at large. The bottom-up approach builds up from data on a hierarchy of disaggregated components (

21、e.g. period of construction, geographical areas as they relate to typical envelope construction and installations), which are then combined according to their estimated individual impact on energy use, weighted by their breakdown in the building stock. These models initiate the analysis at a disaggr

22、egated level by exploiting extensive databases of empirical data. They are based on typical buildings that are representative of the building stock, which are then used to calculate their energy use, assess different EEM and the resulting energy savings and abatement of CO2 emissions. The results ar

23、e estimated per unit floor area and then extrapolated using the total floor area of the corresponding total floor area in the building stock. Findings can then be used for medium- to long-term energy supply strategy. Amongst the main weaknesses of such an approach are the accuracy of the calculation

24、s compared to actual energy use and other assumptions regarding the impact of behavioral factors on actual energy use, for example, the hours of occupancy and use of heating systems, the heated areas, indoor temperature settings, etc. HELLENIC RESIDENTIAL BUILDING TYPOLOGIES Different experiences wi

25、th building typologies have emerged in Europe over the past few years. Amongst a notable effort is the TABULA concept (Loga et al. 2012). A total of 13 national residential building typologies were initially developed following a common methodical structure. Each national typology consists of a clas

26、sification scheme grouping buildings according to their size, location (climate zone) and construction age that relates to energy-relevant building characteristics (e.g. construction, E/M installations), and a set of exemplary buildings representing the respective building types. Representative typo

27、logies serve as an instrument for modelling the energy performance of building portfolios in order to support regional or national energy saving policies. The concept was recently enhanced and extended to 16 national European typologies, including new buildings meeting the national requirements or m

28、ore ambitious standards towards the national NZEB definitions. Accordingly, the Hellenic residential building typologies were defined along the lines of TABULA (Dascalaki et al. 2011) and enhanced for an envisioned NZEB concept, based on relevant progress in other countries, common practices and tre

29、nds available in the literature (Balaras et al. 2014). The 12 typologies that were used to describe the Hellenic building stock, include single family houses (SFH) and multifamily houses (MFH), for different age bands (pre-1980 to reflect the time that the first Hellenic building thermal insulation

30、regulation (HBTIR) was introduced), 1981-2010 partially or fully insulated as a result of compliance with HBTIR and post-2011 in compliance with the new national regulation - KENAK) and the four national climate zones. INSIGHT FROM EPC The attribution of energy performance certificates (EPCs) to bui

31、ldings as a result of EPBD transposition throughout Europe, has initiated the mapping process of the building stock. This is a unique opportunity to collect and organize the necessary data that will reveal key information for accessing and improving buildings. Hellenic EPCs are being issued since Ja

32、nuary 2011, the vast majority of them for buildings or building units rented out (63%) or sold (14%), for the refurbishment of residential buildings (under a national funding programme for energy efficiency of dwellings), and only 0.3% for new buildings. The calculations are performed using the offi

33、cial national software (TEE-KENAK) to issue an official EPC with a building or building unit energy-class (label) based on asset rating. The calculation engine is in accordance to European standards, with the main calculation procedure of the building energy demand estimated using the quasi-steady s

34、tate monthly method (Dascalaki et al. 2012). The tool incorporates the relevant national technical libraries, weather data and other technical specifications outlined in four supporting technical guidelines. Inherent to the calculations are several assumptions according to the national methodology a

35、nd technical guidelines, in order to minimize judgment errors by the software user. By the end of 2014, the national EPC registry was populated with a total of over 460,000 valid certificates for dwellings. Residential building labels are ranked at the lowest energy-class in all four Hellenic climat

36、e zones (Dascalaki et al. 2013), with an average annual total primary (source) energy use of 260 kWh/m2 (82 kBtu/ft2). Although heating degree-days reach over 2600 HDD in the northern parts of the country, only 16% have proper thermal envelope protection (i.e. double glazing and insulated external w

37、alls). Double glazing is common practice in all new buildings and the most frequent refurbishment activity in existing buildings, encountered in about 43% of the dwelling stock (ELSTAT 2013). Actual energy use of the dwellings is only collected and included in the EPCs on a voluntary basis. The raw

38、data for which only one energy carrier is used for space heating and domestic hot water (DHW) were about 12,000 EPCs (of which 15% for SFH) were used for further analysis. The calculated and actual primary energy use for space heating and DHW are normalized for the different size of dwellings by div

39、iding energy use with the heated floor area to obtain the primary energy use intensity (PEUI), as illustrated in Figure 1a, for SFH and MFH. At a second stage, the available raw data were screened with some basic data quality controls, e.g. excluding erroneous (excessively high or low) or questionab

40、le values of thermal or electrical energy consumption. The screened data compose a database of about 7,100 (of which 19% for SFH) that is illustrated in Figure 1b. Further data analysis taking into account the different building typologies in terms of construction period and climate zone was used to

41、 derive empirical adaptation factors f1(actual/calculated). These ratios can be used to make more realistic estimates of the primary energy use or anticipated savings as a result of large scale implementation of ECMs in the building stock, from the calculated values. The corresponding average ratios

42、 are presented in Table 1 for the different Hellenic residential building typologies. The empty cells denote missing information for some specific typologies in the currently available database, i.e. no EPCs are available that include actual energy consumption. Apparently, for some typologies (e.g.

43、recent constructions) there is not enough data and one should exercise caution when using these ratios. However, as the available EPC database with actual energy use data is enriched in the future, one could periodically update the corresponding empirical adaptation factors. (a) (b) Figure 1 Scatter

44、 plots and least-square regression lines of a) raw (left pair of plots) and b) screened (right pair of plots) data for the calculated and actual primary energy use per unit heated floor area for Hellenic SFH and MFH. The 45-degree line (i.e. x=y) identifies the case when the calculated vs actual ene

45、rgy consumption is the same (perfect agreement). As illustrated in Figure 1, the calculated and actual PEUIs exhibit large variations. Some of this scatter is attributed to the unique building characteristics and weather conditions. The prevailing weather conditions also explain some variations of a

46、ctual energy use for the same calculated values, since the calculations are performed using standard weather files for different Hellenic cities, according to the national regulation (KENAK) and the relevant technical guidelines. Actual energy use is also inherently influenced by occupant behavior a

47、nd actual operating conditions that again deviate from the specifications in the calculations. On average, the empirical adaptation factor (Table 1) for SFH is 0.90 for the raw data (i.e. 10% lower energy use than calculated) and 0.54 for the screened data (i.e. 46% lower than calculated). For the M

48、FH, the corresponding values are 1.42 for the raw data (i.e. 42% higher) and 0.57 for the screened data (i.e. 43% lower). Overall, higher calculated PEUIs correspond to lower actual energy use (Figure 1). This is in agreement with the results reported in other studies, a phenomenon identified as the

49、 “prebound” effect (Sunikka-Blank and Galvin 2012), which is more evident for dwellings with a calculated high-PEU (i.e. dwellings with a poor energy performance). The opposite phenomenon is identified as the “rebound” effect (Galvin 2014) and is most notable for low-PEUI dwellings (i.e. dwellings with a good energy performance), with actual energy use higher than the calculated PEUI. Using the derived empirical factors, one could take into account the prebound and rebound effects in order to adapt the calculated energy use and savings from EEMs. FIELD STUDIES The role of occupants is a majo