Aalborg Universitet. CLIMA proceedings of the 12th REHVA World Congress Heiselberg, Per Kvols. Publication date: 2016

Transcription

1 Aalborg Universitet CLIMA proceedings of the 12th REHVA World Congress Heiselberg, Per Kvols Publication date: 216 Document Version Publisher's PDF, also known as Version of record Link to publication from Aalborg University Citation for published version (APA): Heiselberg, P. K. (Ed.) (216). CLIMA proceedings of the 12th REHVA World Congress: volume 6. Aalborg: Aalborg University, Department of Civil Engineering. General rights Copyright and moral rights for the publications made accessible in the public portal are retained by the authors and/or other copyright owners and it is a condition of accessing publications that users recognise and abide by the legal requirements associated with these rights.? Users may download and print one copy of any publication from the public portal for the purpose of private study or research.? You may not further distribute the material or use it for any profit-making activity or commercial gain? You may freely distribute the URL identifying the publication in the public portal? Take down policy If you believe that this document breaches copyright please contact us at vbn@aub.aau.dk providing details, and we will remove access to the work immediately and investigate your claim. Downloaded from vbn.aau.dk on: december 3, 218

2 Simplified Method for estimating Energy Need for Heating and the Minimum Energy Performance Requirements in Buildings through Regression Models D. Iatauro #1, P. Signoretti #, L. Terrinoni # # ENEA Italian National Agency for New Technologies, Energy and Sustainable Economic Development Departement Unit for the Energy Efficiency C.R. Casaccia Via Anguillarese Rome, Italy 1 domenico.iatauro@enea.it Abstract In this report is described an ENEA study aimed to provide technical support to the Ministry of Economic Development within the implementation of European Directive on the energy efficiency of the buildings (21/31/EU) A simplified methodology, aimed to evaluate the energy need for heating, by mean simple parameters, is proposed. The energy demand has been estimated through a standardized procedure according to the Italian legislation for energy certification of buildings. This method can represent an useful tool for the legislator, allowing to verify, in a quick way, as the variation of few main parameters can modify the energy demand. Considering different reference buildings, it has been carried out a numerical simulation to calculate the energy need for heating through the methodology given by national technical standards. The tests have been done locating the buildings in different reference sites, representative of different climatic zones of the Italian climate. It was subsequently defined a calculation procedure based on regression methods to evaluate the heating energy need starting from few parameter. Regression analysis has showed the goodness of fit of the model, and has been highlighted the possibility of using it also for the estimation of the energy performance in different conditions of calculation. Keywords - Heating Energy Need; Certification of Buildings; Regression models; 1. Introduction The European Directive EPBD 21/31/EU asked to EU Member States to improve the energy efficiency measurements in the building sector, that accounting the 4% of total energy consumptions in the Union and has a significant energy saving potential. The definition of minimum requirements for the energy performance of buildings, developed in accordance with the cost-optimal methodology, has increased the minimum efficiency standards required for new constructions and buildings elements, in order to achieve progressively, in the next years, the target of Nearly Zero Building (nzeb), as prescribed by EPBD. The followed procedure, to define standard requirements, has been carried out taking into account the energy need for heating of a reference building models set, located in five different zones (15 towns), representative of the Italian climate. The calculation procedure was given by national technical standards UNI-113. It is based on a quasi-steady-state methodology, and provides the seasonal energy demand needed to maintain the space heating at a temperature of 2.

3 Given the wide range of building typology, the reference models have been selected among the most common in the Italian building stock and regardless of specificity of some categories of buildings. The aim of the this study was to define a simple way to correlate the energy demand of building with the thermal characteristics of envelope and the climate of site. The procedure proposed, based on statistical regression methods, allow to estimate the energy need of building with different envelope requirements and quickly comparing the energy performance of various design alternatives. In this paper is described the approach. 1. Climate data and Reference buildings models The Italian territory, is characterized by great climatic variety. For this reason it was divide in six different climatic zones as a function of degree day (A-F). The distribution of Italian municipalities show that the major amount (92%) falls in the three zones: C, D, E; the remaining part, 8%, falls almost entirely in the zone B. In order to consider different climate conditions in the calculation of the energy need for heating, in this study, have been selected 15 towns in f zones (3 for each zone from B to F): Figure 1

4 The length of the heating season and the operation time of the heating systems, in Italy, are given by law (Dlgs 412/93). The following table reports the heating period for the five climatic zones, the number of the day of heating season and the main climatic parameters of the selected towns. Number of Day Solar Radiation (kwh/m²) Zones Locations Heating period T mean ( C) REGGIO CALABRIA 1-Dec 31-Mar , B CROTONE 1-Dec 31-Mar ,5 SAPONARA 1-Dec 31-Mar ,7 C D E F LECCE 15-Nov 31-Mar ,4 CATANZARO 15-Nov 31-Mar ,7 CALTAGIRONE 15-Nov 31-Mar ,6 TERNI 1-Nov 15-Apr ,9 FORLI' 1-Nov 15-Apr ,5 CASTIGLIONE D L. 1-Nov 15-Apr , ROVIGO 15-Oct 15-Apr , AOSTA 15-Oct 15-Apr ,8 CASINA 15-Oct 15-Apr ,9 BELLUNO 5-Oct 22-Apr ,5 CALASCIO 5-Oct 22-Apr ,4 SESTRIERE 5-Oct 22-Apr ,9 Table 1 The reference buildings models, selected for the calculation procedure, includes three main typology, very common in the Italian stock building: Detached house (single floor) Small multifamily building (block of 8 flats) Large multifamily building (block of 24 flats) For each building model have been defined three insulation level: starting from corresponding to U-value limits for building envelope, required in national legislation (level 1), have been assumed increasing values for thermal transmittance (level 2-3). The thermal performance of building envelope, can be expressed by representative index H, the overall transmission heat transfer, defined as follow: ' T Where : H ' tr, adj 2 H T = W / m K (1) S

5 H tr, adj is the heat loss for transmission coefficient of building to the external environmental and S = A is the total area of elements of the building K K envelope. The table 2 summarize the minimum, maximum and mean values of assumed in the calculation procedure. Overall transmission heat transfer H T (W/m 2 K) Max Mean Min Detached house,55,41,3 Small block of flats,67,49,37 Big block of flats,72,53,4 Table 2 The main geometric characteristics of the building models are schematically described in the table 3: Geometric characteristics of building models Detached house Small multifamily building Large multifamily building Total height [m] Length [m] 11, 2, 2, Width [m] 11, 1, 15, Ground floor [m 2 ] 97, Windows [m 2 ] 12,6 64,8 257 Net Volume [m 3 ] Gross Volume [m 3 ] Heat transfer surface [m 2 ] 365, S/V [m -1 ],99,61,42 Table 3 ' H T The ventilation rate is,5 h -1 for all building models in accordance UNI 1339 Considering 3 building typology, 3 insulation levels of envelope, and 15 locations (three for each climate zone), the calculation procedure, were based on (3x3x15) 135 simulations different configurations of buildings.

6 2. Energy Need for Heating and Minimum Energy Performance Requirements The energy need for heating, according to UNI 113-1/214 (Asset Rating), represent the heat to be delivered to a conditioned space to maintain the intended temperature conditions ( θ int,set =2 C) during the heating season. It s can be calculated, in standardized way (conditions of continuous heating), starting from the energy balance at building level (without considering the efficiency of the heating systems) through the follow equation: Where: Q H, tr Q = Q + Q η ( Q + Q ) (2) H, nd H, tr H, ve H, gn int sol, w : transmission heat transfer between the conditioned space and the external environment Q : ventilation heat transfer by natural ventilation or by a H, ve mechanical ventilation system Q :internal heat gains int Q : solar heat gains through windows sol, w η : loss utilization factor H, gn The numerical simulations were carried out using a stationary calculation code, in accordance with the technical standards UNI TS The analysis of the heat flows has showed that transmission heat transfer component Q H, tr, represents clearly the main term in the energy balance of the building, for all the reference models considered (Figure 2). Being Q H, tr function of the product of Degree Day (DD) of the site, total heat transfer area between the envelope and the environment S, and (defined as before), also the total energy need QH, nd depends essentially by the same parameters (Figure 2). The equation 3, expresses what has been said: Where Q Q H ( θ θ ) t (3) int, set θe t of the heating season. H, nd H, tr tr, adj int, set e ' H T ( θ ) is equivalent to Degree Day of the site if t is the length

7 Figure 2 This entail that the energy need for heating per unit of volume is proportional to tree main terms: the overall heat transmission transfer H ' T, the shape factor of building S V, and the Degree Day of the site DD: QH, nd ' S = g H T DD = g ( ξ DD ) (4) V V ' S Starting from this relationship, and introducing ξ = HT V, were carried out for all reference buildings a linear regression in order to evaluate the correlation between Q H, nd the V ξ DD and ( ) 45 4 Linear regression prediction interval,95 35 Q H,nd /V(kWh/m 3 ) y =.22x R² =.981 All buildings models DD ξ (Wd/m 3 ) Figure 3

8 The goodness of fit of the linear model is highlighted in the figure 3, in which the results of regression are reported with prediction interval of 95%. The clear linear correlation obtained ( R 2 =,98) prove that the energy need for heating, evaluated in standardized way (UNI TS 113), is substantially independent of the type of building considered, and is directly proportional to the product ( DD) ξ as defined before. Q H, nd V =, 22 DD ξ 3,89 (5) The simple relation defined by equation (5), provides an useful tool to support national authority in phase of definition of the energy performance requirements of the building, since it allows to quickly verify how the variation of few parameters can modify the energy need for the heating of buildings. As shown in the figure 4, the equation of the regression line can be also used as a tool to evaluate a variation trend of the energy demand comparing buildings with different characteristics of envelope or different climate conditions High energy need Q H,nd / V(kWh/m 3 ) % % DD ξ 1 5 Low energy need Figure 4 Using the simplified relationship obtained, if is known the climate zone of site, for a given range of the energy need, it s possible to derive the minimum energy performance requirements for the building envelope and consequently define the U- value limits for all building elements.

9 3. Regression model Analysis Despite the evidence of the linear correlation, as shown by the high value of coefficient of determination R 2, for a further evaluation of the significance of the regression model adopted, statistical tests were carried out on residuals values and regression coefficients. The assumptions underlying the linear model imply that the residuals of the regression should have a normal distribution (of zero mean), constant variance, be independent and doesn't correlated with each other (autocorrelation).. Residuals distribution Normality Test Residuals distribution 3 28 Saphiro-Wilk Test = , p = No of obs Residuals Theoretical values Figure 7 Figure 8 The shape of the histogram in the figure 7 and results of Saphiro-Wilk test, prove the normality of errors; in figure 8 the vertical distribution of residuals values highlights the independence and the absence of autocorrelation. Furthermore, the statistical significance of regression parameters has been verified by Fischer F-test (ANOVA) (y=ax + b; H : a= ; H 1 : a ). In the following table are reported the results of test. gdl SQ MQ F P-value Regression , , ,97 1,258E-116 Residual ,46 1,13 Total ,89 Table 6 The P-value, far lower than level of significance (,5) allows to reject the hypothesis H and confirm the validity of the linear regression model.

10 4. Further application of linear regression model For a further test of regression model, the results obtained for residential buildings have been compared, considering the same locations, with an office building, with U-values in accordance with the current limits required by law, and having the follow characteristics: Overall transmission heat transfer H T (W/m 2 K) Office building Level min Level mean Level max,68,81 1, Table 4 Office building Windows [m 2 ] 435 Net Volume [m 3 ] 41 Vertical walls [m 2 ] 61 Ground floor [m 2 ] 48 Total dissipation area [m 2 ] 23 S/V [m -1 ],35 Table 5 The ventilation rate is,88 h -1, according to UNI 1339 for office building Qh,nd/V kwh/m y =.439x R² =.9778 y =.225x R² =.9812 Residential Office 5 DD ξ (Wd/m 3 ) Figure 6 The linear correlation seems to be good also in this case, but different slope of the ξ DD the office building has, in regression line mean that, for a given value of ( ) general, a more high energy need. This is due to the higher proportion of the ventilation heat loss in the energy balance. However this effect, in the range of

11 ( DD ) ξ < 5, is balanced by higher contribution of solar gains through the glass surfaces, generally larger than residential buildings. The different trend observed, compared to the previous cases, suggests to evaluate non-residential buildings separately and to carry out further analysis to investigate the wide range of building type in this sector. 5. Conlclusions In this study was described a simplified method, based on regression models, which provides estimates of the energy need for heating of buildings as a function of the degree-days (DD), characteristics of climate conditions of site, and the parameter ξ, representative of thermal performance of the building. The energy demand has been calculated by the quasi-steady-state calculation methods (UNI TS 113) considering 3 different reference buildings, 3 insulation levels, and 15 locations representative of the Italian climate. The analysis of least squares linear regression, carried out on the whole reference buildings set (135 configurations), has highlighted the goodness of fit of the model, confirmed by statistical tests on residuals values and on the regression coefficients. The use of simplified method proposed, can represent an useful tool, not only to estimate the energy demand for heating, but also, for the national authority, to define the energy performance requirements of the building envelope, and to verify, in a quick way, as the variation of few main parameters, can affect the energy demand of building, and determine its classification. In order to evaluate the effectiveness of model on different buildings type, the linear regression model was also applied to an office building. The test has showed a good linear correlation, but the different trend observed, compared to the previous cases, suggests to consider office buildings separately. Further studies are needed, to analyze different types of non-residential buildings and to assess the application of regression models for estimating the energy needs for cooling in summer season. References [1] A European Directive on Energy Performance of Buildings 21/31/EU [2] Italian Standard UNI/TS 113-1, Energy performance of buildings: Evaluation of energy need for space heating and cooling,-214 [3] Italian Standard UNI 1349/94 Riscaldamento e raffrescamento degli edifici Dati climatici, 1994 [4] D.P.R. 26 agosto 1993 n. 412 Regolamento recante norme per la progettazione, l'installazione e la manutenzione degli impianti termici degli edifici, ai fini del contenimento dei consumi di energia [5] Modelli di regressione per la stima dei fabbisogni energetici per la climatizzazione degli edifici, P. Signoretti, L. Terrinoni, D. Iatauro ENEA Report RdS 215 [6] Modern Regression Analysis For Scientists and Engineers Thomas P.Ryan, National Institute of Standards of Technology Gaithersburg, MD 23 [7] Probability and Statistics for engineers & scientists, Ronald E. Walpole Raymond H. Myers S. L. Myer