- Email: [email protected]
Investigation on the influencing factors of energy consumption and thermal comfort for a passive solar house with water thermal storage wall
Energy and Buildings 64 (2013) 218–223
Contents lists available at SciVerse ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Investigation on the influencing factors of energy consumption and thermal comfort for a passive solar house with water thermal storage wall Weiliang Wang, Zhe Tian, Yan Ding ∗ School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
a r t i c l e
i n f o
Article history: Received 7 September 2012 Received in revised form 3 April 2013 Accepted 4 May 2013 Keywords: Passive solar house Water thermal storage wall Orthogonal analysis Variance analysis
a b s t r a c t A passive solar house (PSH) could fully receive and store the incident radiation by the rational arrangement of the building structure and the utility of the massive construction materials. The influence of water thermal storage wall (WTSW) on the indoor thermal environment is analyzed in this paper. Besides, different parts of the building envelope exert varying degrees of impact on the building energy consumption and indoor thermal comfort. Research on the influencing factors could provide references for the building energy conservation design and the retrofit of existing buildings. A PSH with interior walls of WTSW is studied, which is currently being used in North China. Field measurements were carried out in the reference building. TRNSYS was used to simulate the variation of indoor air temperature. The results of simulation and orthogonal analysis indicate that compared to traditional wall the PSH equipped with WTSW can reduce yearly energy consumption by 8.6% and improve indoor thermal comfort evaluation index by 12.9%. Meanwhile, significance of four different structural parameters (namely the shape coefficient, building orientation, glazing ratio of the south wall and the interior wall structure) respectively on the energy consumption and thermal comfort is obtained by the variance analysis. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Building energy consumption in European countries accounts for 40% of the total social energy use [1], while in New York reaches up to 2/3 [2]. Since building energy consumption is able to be reduced significantly by fully consideration of the heating and cooling loads at building design stage, much more attentions are paid on the optimization of the building envelopes than ever before [3]. The influencing factors of the building envelope generally include the outline dimension, orientation, glazing area and thermo-physical properties of construction materials [4]. Heat exchange area with the outdoor air is determined by the building outline dimension. Wang et al. [5] researched the impact of flat shape on the building life cycle cost. Ourghi et al. [6] considered that a strong correlation existed between the yearly energy consumption and the building shape coefficient, which was defined as the ratio of external surface area over the inner volume. In addition, Depecker et al. [7] regarded the above strong correlation to be effective only in the places with large heating degree days or a short duration of sunshine. Besides, Marks [8] and Adamski [9] optimized the outline dimension from the building costs and yearly
∗ Corresponding author. Tel.: +86 13821601196; fax: +86 02287891898. E-mail address: [email protected] (Y. Ding). 0378-7788/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.enbuild.2013.05.007
heating costs. Mingfang [10] studied the optimal building form basing on the solar heat control in summer and Aksoy et al. [11] researched the influence of shape factor on the heating demand in winter. Solar energy received by building depends on its orientation and glazing area. Morrissey et al. [12] considered that buildings with high energy efficiency normally owned a wide variation range in orientation. Researches of Shaviv [13] and Florides et al. [14] showed that the wall with a maximum glazing ratio (namely the ratio of window to wall) or with a longer edge should face south in hot and humid areas. Littlefair [15] considered that the optimal orientation can have an angle of less than 20–30◦ with due south. As to the glazing area, Persson et al. [16] found an appropriate glazing ratio after the optimization on the window. While in the opinion of Leskovar et al. [17,18], the optimal glazing ratio was determined by the heat transfer performance of the building envelope. Properties of building material directly influence the heat transfer and heat storage capacity of the wall. Oral et al. [19] introduced a method to determine the limit value of the wall heat transfer coefficient. Lollini et al. [20] and Jinghua et al. [21] studied the influence of the wall thermal insulation on the energy consumption. And C¸omakli et al. [22] investigated the optimum insulation thickness of the exterior wall. Khalifa [23] analyzed the sensitivity of the indoor air temperature to the change in the thermal storage
W. Wang et al. / Energy and Buildings 64 (2013) 218–223
219
Fig. 1. Typical structure form of the Trombe wall.
wall. Hassanain et al. [24] researched the passive heating effect of heat storage wall in passive solar houses with different roof shapes. While Khalifa et al. [25] and Gregory et al. [26] studied the massive wall with different construction material. Trombe wall is an effective form to exploit the thermal storage capacity. The related researches were conducted in the thermal performance comparison with transparent modular wall [27], the comparison of different types of Trombe wall [28], the winter passive heating effect [29], the summer passive cooling effect [30] and the reduction in energy consumption and CO2 emissions [31]. Typical structure form of the Trombe wall is shown as Fig. 1 [32]. Kinds of materials are suitable for filling in the massive wall in Fig. 1, one of which is water [33]. During construction process, water thermal storage wall (WTSW) is usually built by enclosing the water with concrete [34] or stainless steel plate. There are little literatures concerning on this kind of high thermal capacity but low-cost wall. Moreover, only single influencing factor of the building envelope was concerned in the previous researches and a comprehensive evaluation is in great need. Thus, a case study was conducted on a passive solar house (PSH) with WTSW in North China. A simulation model is also established with TRNSYS in order to study the influence of WTSW on the indoor thermal environment as well as the energy consumption. Orthogonal analysis is designed to compare the influencing significance of four different structural parameters, including the shape coefficient, building orientation, glazing ratio of the south wall and the interior wall structure, with the help of variance analysis. 2. Description of the reference building 2.1. Introduction of the passive solar house with water thermal storage wall The single-storey passive solar house faces due south, which has a floor area of 700 m2 and a shape coefficient (the ratio of external surface area to inner volume) of 0.374. It is located in Tianjin, North China. The house is divided into two zones, which can be seen in Fig. 2. A sun space corridor with a width of 1.4 m lies on the outside margin of the south zone, which can receive solar radiation as much as possible with a glazing ration of 100% in the south wall. The WTSW, outer wall made of steel plate, is processed into modular production with a dimension of 1.1 m × 0.4 m × 2.5 m (length × width × height). WTSW modules are placed along the inner side of the corridor besides some of the interior walls, which are shown by the shaded portion in Fig. 2. Total 29 modules are used in the PSH to store the received solar heat during the daytime. External and other interior walls consist of the steel construction and gypsum boards with a total thickness of 60 mm. 2.2. Simulation and validation of the reference building model According to the description, a building model was established in TRNSYS to simulate the hourly air temperature in different zones.
Fig. 2. Floor plan of the passive solar house with water thermal storage wall.
Weather data from the Tianjin Meteorological Bureau was adopted in the simulation. In order to validate simulation results and evaluate the effect of the WTSW, the indoor air temperature has been continuously monitored for more than one week from November 18th to 27th. Four temperature data loggers were fixed at point A–D as shown in Fig. 2. The average temperature of different zones by simulation and field test in the PSH are shown respectively in Fig. 3. In the condition of no space heating, the measured indoor temperature remained at 13.7 ◦ C mainly because of the high thermal capacity of the WTSWs even when the outside air dropped to −0.4 ◦ C (7:00 am November 23rd). The simulated temperature had a maximum relative error of 24.5% compared to the measured values. Such error is probably related with the randomness of human’s activity, such as opening or closing the door, turning on or off the lights and so on. An average relative error of 8.97% reflects the accuracy of the simulation, so this model is suitable for the later orthogonal analysis. 3. Orthogonal analysis 3.1. Test factors and levels Based on the literature review, four structural parameters are chosen as the test factors, namely the shape coefficient (SC), building orientation (BO), glazing ratio of the south wall (GR) and interior wall structure (IS). The low levels of each factor are the real dimensions of the reference building in North China, while the high levels are the limitations regulated in design standards of energy efficient residential buildings. The medium levels are the average values of the low levels and high levels. All the factor levels are listed in Table 1. Among which, the realistic orientation barely has an angle of more than 30◦ with due south, and the levels of glazing ratio and shaping coefficient are determined by referring to the energy saving design standard [35] and the original values of the reference building. Besides, the ordinary wall with a layout illustrated in Table 2 is a popular form used in practice, which can meet the requirement of the energy saving standard.
220
W. Wang et al. / Energy and Buildings 64 (2013) 218–223
Fig. 3. Comparison results of the simulating and measuring temperature.
Table 1 Test factors and the corresponding levels. Levels
1 2 3
Factors SC Shape coefficient
BO Building orientation
GR Glazing ratio of south wall
IS Interior wall structure
0.374 0.437 0.52
−30◦ (Southwest) 0◦ (Due south) 30◦ (Southeast)
100% 75% 50%
Ordinary wall WTSW –
3.2. Introduction of the test index The energy consumption and thermal comfort are studied respectively by orthogonal analysis. 3.2.1. New degree days Human body can adapt to a changing environment. Humphreys and Nicol [36] proposed an adaptive model as shown in Eq. (1) for the indoor thermal comfort temperature tcomf , where tout is the outdoor monthly average temperature. While de Dear and Brager [37] simplified the model under the condition of no space heating or cooling. The simplified model is described as Eq. (2). The dissatisfied percentage will be less than 20% even if the indoor temperature varies between tcomf ± 3.5 ◦ C [37], thus the range of tcomf can be determined.
t − 22 2 out
tcomf = 24.2 + 0.43(tout − 22) exp −
√ 24 2
tcomf = 0.31tout + 17.8
(1) (2)
Degree days (DDs) are widely used to measure the energy consumption in different outside conditions [38]. In this study, new degree days (NDDs), which are the hourly accumulation of the absolute difference between the indoor temperature tair and the limits of tcomf during a year as described in Eq. (3), are used to measure the yearly energy consumption under different indoor conditions. Because the thermal capacity of the building enve-
lope may change when the test factors take different levels, NDDs become an appropriate index of the building energy consumption due to the independence of the heat capacity.
NDDs =
⎧
[tair − (tcomf + 3.5)], tair > tcomf + 3.5 ⎪ ⎨ 0,
⎪ ⎩
tcomf − 3.5 < tair < tcomf + 3.5
[(tcomf − 3.5) − tair ],
(3)
tair < tcomf − 3.5
3.2.2. Discomfort index Thermal comfort sense is influenced by the steady state and dynamic factors. The former refers to the absolute deviation of the effective temperature and the optimum temperature, while the latter represents the relative change rate of the absolute deviation upon the time. These two factors are fully considered in the discomfort index (DI), which is shown in Eqs. (4)–(6) [39]. DI =
MSE + 5MSR 6
MSE =
m =1
MSR =
E2 =
m
(4)
(ET − PT)2
(5)
=1
m E − E−1 =1
(6)
2
Table 2 Layout of the ordinary wall. Materials
Density (kg/m3 )
Conductivity (W/(m K))
Capacity (J/(kg K))
Thickness (m)
Cement mortar Concrete hollow block Insulation mortar
1800 1300 250
0.93 0.68 0.06
1050 538 1050
0.02 0.2 0.02
W. Wang et al. / Energy and Buildings 64 (2013) 218–223
221
Table 3 Orthogonal analysis table and the calculation results of the test indexes. Test no.
Factors
1 2 3 4 5 6 7 8 9
Test indexes
SC Shape coefficient
BO Orientation
GR Glazing ratio
IS Interior wall structure
NDDs ◦ C·days
DI –
1 1 1 2 2 2 3 3 3
1 2 3 1 2 3 1 2 3
1 2 3 2 3 1 3 1 2
1 2 3 3 1 2 2 3 1
20146.7 17874.7 24026.6 24220.2 25487.3 16535.1 28060.7 19672.8 23140.2
23,172 18,381 29,516 31,462 32,970 16,121 38,837 22,363 28,070
where, MSE and MSR represent the steady state factor and dynamic factor, and m mean the current hour and total hours, ET is the effective temperature, PT is the optimum temperature and E is the absolute deviation of ET and PT. The constant optimum temperature of 16 ◦ C and 14 ◦ C were recommended for the daytime and night time separately [39], which ignored the physically adjusting ability of the human body. Thus, in this paper, the lower and upper limits of tcomf are chosen for PT in winter and summer respectively. 4. Results and discussion 4.1. Orthogonal analysis table and results According to the test factors and levels, the L18 (21 × 37 ) orthogonal arrays are originally required mostly because factor IS has only two levels. The orthogonal analysis could be optimized by the quasi level method. Specifically, a pseudo level of IS3, which is chosen the same as IS1 of the ordinary wall, is added for factor IS. Thus the L9 (34 ) orthogonal analysis table can be used as shown in Table 3. Although reducing the simulation work largely, the new arrays do not have the orthogonality any more because of the same two levels of factor IS. But special handling on the variance analysis in Section 4.2.2 can ensure the feasibility of the significance study [40]. Totally 9 groups of conditions are simulated and the results of those two test indexes are listed in Table 3. 4.2. Analysis on the orthogonal analysis results 4.2.1. The influence of WTSW on energy use and thermal comfort index The influence of the new type of thermal storage wall is studied comparing to the ordinary wall. Both IS1 and IS3 represent the ordinary wall in Table 3, while IS2 represent the thermal storage wall. The index results of factor IS with the same level in Table 3 are averaged and then listed in Table 4. From which, the WTSW surpasses the ordinary wall which is widely used in China both from
Table 4 Influence of WTSW on energy use and thermal comfort index. Levels of factor IS
Implication
NDDs ◦ C·days
DI –
IS1 & IS3 IS2
Ordinary wall WTSW
22782.3 20823.5
28,070 24,446
the energy saving and thermal comfort aspects. For the NDDs and DI represent the annually heating and cooling energy consumption and the thermal discomfort level respectively, the quantitative results in Table 4 indicate that the PSH with WTSW can cut 8.6% of the yearly energy consumption while raise the comfort evaluation index by 12.9%. 4.2.2. Variance analysis of the test factors Variance analysis is carried out as below. Firstly, the sum of deviation square (SS) is discomposed by the degree of freedom (DF) into mean squares (MS). Statistical variables of F-test are constructed by the mean square ratio of the different factors and the error. Then the significance of factors can be obtained. The calculation method of each item in the variance analysis table is listed in Appendix. Among which, the SS of factor IS is calculated in the same formula with other factors except that the level amount of k should takes the actual amount (kD = 2 in this paper). Thus, the test numbers of factor IS in level 1 and 2 are 6 and 3 respectively, and the corresponding DF equals 1. The variance analysis of NDDs and DI are respectively shown in Tables 5 and 6. In the variance analysis table, F-value reflects the impact of a factor compared with that of the error on the test index. Since the influence of the error is generally small, the more the F-value is close to 1, the less significance of the factor will be. According to the variance analysis results, the order of the significance degree can be obtained as following. With regard to the influence on the energy consumption, the order is GR (glazing ratio) > BO (building orientation) > IS (interior wall structure) > SC (shape coefficient). As to the thermal comfort, the order is GR (glazing ratio) > BO (building orientation) > SC (shape coefficient) > IS (interior wall structure). So
Table 5 Variance analysis table with index of NDDs. Items
SS
DF
MS
SC BO GR IS Error Sum
12992101 18312102 75713108 7673988 121732 114813031
2 2 2 1 1 8
6496050 53.4 49.5 9156051 75.2 49.5 37856554 310.9 49.5 7673988 63.0 39.9 121732 F0.1 (2,1) = 49.5, F0.05 (2,1) = 199.5, F0.01 (2,1) = 4999.5 F0.1 (1,1) = 39.9, F0.05 (1,1) = 161.4, F0.01 (1,1) = 4052.2
F-value
Critical value of F-value 0.1
* **
The significance level between 0.1 and 0.05, which can be deemed as a moderately significant influencing factor. The significance level between 0.05 and 0.01, which can be deemed as a highly significant influencing factor.
Significance
0.05
0.01
199.5 199.5 199.5 161.4
4999.5 4999.5 4999.5 4052.2
* * ** *
222
W. Wang et al. / Energy and Buildings 64 (2013) 218–223
Table 6 Variance analysis table with index of DI. Items
SS
DF
MS
F-value
Critical value of F-value 0.1
SC BO GR IS Error Sum *
55252873 86776473 265099313 24209274 126025.08 431463957
2 2 2 1 1 8
27626437 219.2 49.5 43388236 344.3 49.5 132549656 1051.8 49.5 24209274 192.1 39.9 126025.1 F0.1 (2,1) = 49.5, F0.05 (2,1) = 199.5, F0.01 (2,1) = 4999.5 F0.1 (1,1) = 39.9, F0.05 (1,1) = 161.4, F0.01 (1,1) = 4052.2
Significance
0.05
0.01
199.5 199.5 199.5 161.4
4999.5 4999.5 4999.5 4052.2
** ** ** **
The significance level between 0.1 and 0.05, which can be deemed as a moderately significant influencing factor. ** The significance level between 0.05 and 0.01, which can be deemed as a highly significant influencing factor.
both from the view of energy saving and thermal comfort, the glazing ratio and building orientation are the two most important parts for designers to focus on. Besides, even the water thermal storage wall owns large benefits compared to the ordinary walls, the interior wall structure is not the most important factor among those four structure parameters.
5. Conclusions With the increasingly large amount of the building energy consumption, optimization design on the building envelope becomes an effective method to reduce the building energy demand. This paper studied a passive solar house in use that comprised a new kind of water thermal storage wall. The reference building model was established with TRNSYS. Basing on the model, the orthogonal analysis was designed to study the influence of the WTSW and the impact significance degrees of different structure parameters. Conclusions are obtained as following. (1) The indoor environment with WTSW is better than that with ordinary wall. The superiority arises both from the view of energy saving and the thermal comfort. The studied PSH with WTSW reduce 8.6% of the yearly energy consumption and raise the thermal comfort evaluation index by 12.9%. (2) With regard to the energy consumption index of NDDs, the influence significance order of the building envelope parameters is the glazing ratio, building orientation, the interior wall structure and shape coefficient. (3) As to the thermal comfort index of DI, which indicates the thermal discomfort, the influence significance order becomes the glazing ratio, building orientation, shape coefficient and the interior wall structure.
Acknowledgments This research has been supported by National Science and Technology Support Program (Grant No. 2011BAJ03B00).
Appendix A.
⎛ n ⎜ ⎜ ⎜yi − SST = ⎜ i=1 ⎝
DFT = n − 1
n i=1
n
⎞2 yi ⎟
⎟ ⎟ ⎟ ⎠
(A.1)
(A.2)
⎛
kfactor
SSfactor =
i=1
⎜ ⎜ ni ⎜ ⎜Ti − ⎝
n i=1
n
⎞2 yi ⎟
⎟ ⎟ ⎟ ⎠
(A.3)
DFfactor = kfactor − 1
(A.4)
SSe = SST −
(A.5)
DFe = DFT −
SSfactor
DFfactor
(A.6)
MS = SS/DF
(A.7)
Ffactor = MSfactor /MSe
(A.8)
where SS is the sum of deviation square, DF is the degree of freedom, MS is the mean square, F is the statistical variable of F-test. As to the subscript, T means total, factor includes test factor of SC, BO, GR and IS. e is the error. In the formula, yi is the test result of the group i, n is the total number of test groups, kfactor is the level number of a factor, ni is number of test groups in level i, T¯ i is the average test results in level i. References [1] H.-X. Zhao, F. Magoulès, A review on the prediction of building energy consumption, Renewable and Sustainable Energy Reviews 16 (6) (2012) 3586–3592. [2] B. Howard, L. Parshall, J. Thompson, S. Hammer, J. Dickinson, V. Modi, Spatial distribution of urban building energy consumption by end use, Energy and Buildings 45 (2012) 141–151. [3] R. Pacheco, J. Ordo˜nez, G. Martinez, Energy efficient design of building: a review, Renewable and Sustainable Energy Reviews 16 (2012) 3559–3573. [4] B.B. Ekici, U.T. Aksoy, Prediction of building energy needs in early stage of design by using ANFIS, Expert Systems with Applications 38 (2011) 5352–5358. [5] W. Wang, H. Rivard, R. Zmeureanu, Floor shape optimization for green building design, Advanced Engineering Informatics 20 (2006) 363–378. [6] R. Ourghi, A. Al-Anzi, M. Krarti, A simplified analysis method to predict the impact of shape on annual energy use for office buildings, Energy Conversion and Management 48 (2007) 300–305. [7] P. Depecker, C. Menezo, J. Virgone, S. Lepers, Design of buildings shape and energetic consumption, Building and Environment 36 (2001) 627–635. [8] W. Marks, Multicriteria optimisation of shape of energy-saving buildings, Building and Environment 32 (4) (1997) 331–339. [9] M. Adamski, Optimization of the form of a building on an oval base, Building and Environment 42 (2007) 1632–1643. [10] T. Mingfang, Solar control for buildings, Building and Environment 37 (2002) 659–664. [11] U.T. Aksoy, M. Inalli, Impacts of some building passive design parameters on heating demand for a cold region, Building and Environment 41 (2006) 1742–1754. [12] J. Morrissey, T. Moore, R.E. Horne, Affordable passive solar design in a temperate climate: an experiment in residential building orientation, Renewable Energy 36 (2011) 568–577. [13] E. Shaviv, The influence of the orientation of the main solar glazing on the total energy consumption of a building, Solar Energy 26 (1981) 453–454. [14] G.A. Florides, S.A. Tassou, S.A. Kalogirou, L.C. Wrobel, Measures used to lower building energy consumption and their cost effectiveness, Applied Energy 73 (2002) 299–328.
W. Wang et al. / Energy and Buildings 64 (2013) 218–223 [15] P. Littlefair, Daylight, sunlight and solar gain in the urban environment, Solar Energy 70 (3) (2001) 177–185. [16] M.-L. Persson, A. Roos, M. Wall, Influence of window size on the energy balance of low energy houses, Energy and Buildings 38 (3) (2006) 181–188. [17] V.Z. Leskovar, M. Premrov, Influence of glazing size on energy efficiency of timber-frame buildings, Construction and Building Materials 30 (2012) 92–99. [18] V.Zˇ . Leskovar, M. Premrov, An approach in architectural design of energyefficient timber buildings with a focus on the optimal glazing size in the south-oriented fac¸ade, Energy and Buildings 43 (12) (2011) 3410–3418. [19] G.K. Oral, Z. Yilmaz, The limit U values for building envelope related to building form in temperate and cold climatic zones, Building and Environment 37 (2002) 1173–1180. [20] Lollini, Barozzi, Fasano, Meroni, Zinzi, Optimisation of opaque components of the building envelope energy, economic and environmental issues, Building and Environment 41 (2006) 1001–1013. [21] Y. Jinghua, Y. Changzhi, T. Liwei, Low-energy envelope design of residential building in hot summer and cold winter zone in China, Energy and Buildings 40 (2008) 1536–1546. [22] K. C¸omakli, B. Yüksel, Optimum insulation thickness of external walls for energy saving, Applied Thermal Engineering 23 (2003) 473–479. [23] A.-J.N. Khalifa, A study on a passive heated single-zone building using a thermal storage wall, Energy Conversion and Management 39 (1998) 1597–1605. [24] A.A. Hassanain, E.M. Hokam, T.K. Mallick, Effect of solar storage wall on the passive solar heating constructions, Energy and Buildings 43 (2/3) (2011) 737–747. [25] A.J.N. Khalifa, E.F. Abbas, A comparative performance study of some thermal storage materials used for solar space heating, Energy and Buildings 41 (4) (2009) 407–415. [26] K. Gregory, B. Moghtaderi, H. Sugo, A. Page, Effect of thermal mass on the thermal performance of various Australian residential constructions systems, Energy and Buildings 40 (2008) 459–465.
223
[27] J.K. Nayak, Transwall versus Trombe wall: relative performance studies, Energy Conversion and Management 27 (4) (1987) 389–393. [28] J. Shen, S. Lassue, L. Zalewski, D. Huang, Numerical study on thermal behavior of classical or composite Trombe solar walls Classical Trombe wall, Energy and Buildings 39 (2007) 962–974. [29] H. Onbasioglu, A.N. Egrican, Experimental approach to the thermal response of passive systems, Energy Conversion and Management 43 (2002) 2053–2065. [30] G. Gan, A parametric study of Trombe wall for passive cooling of buildings, Energy and Buildings 27 (1998) 37–43. [31] A. Chel, J.K. Nayak, G. Kaushik, Energy conservation in honey storage building using Trombe wall, Energy and Buildings 40 (9) (2008) 1643–1650. [32] S. Jaber, S. Ajib, Optimum design of Trombe wall system in mediterranean region, Solar Energy 85 (9) (2011) 1891–1898. [33] G.N. Tiwari, Y.P. Yadav, S.A. Lawrence, Performance of a solarium: an analytical study, Building and Environment 23 (2) (1988) 145–151. [34] S.C. Kaushik, S. Kaul, Thermal comfort in buildings through a mixed water-mass thermal storage wall, Building and Environment 24 (1989) 199–207. [35] GB50189-2005 Design Standard for Energy Efficiency of Public Buildings, China Architecture and Building Press, Beijing, 2005 (in Chinese). [36] J.F. Nicol, M.A. Humphreys, Adaptive thermal comfort and sustainable thermal standards for buildings, Energy and Buildings 34 (2002) 563–572. [37] R.J. de Dear, G.S. Brager, Thermal comfort in naturally ventilated buildings: revisions to ASHRAE Standard 55, Energy and Buildings 34 (2002) 549–561. [38] G.J. Schoenau, R.A. Kehrig, Method for calculating degree-days to any base temperature, Energy and Buildings 14 (4) (1990) 299–302. [39] Y.-Z. Li, Passive Solar House Thermo-technical Design Manual, Tsinghua University Press, Beijing, 1993 (in Chinese). [40] X.-M. Zhao, Experimental Design Methods, Science Press, Beijing, 2010 (in Chinese).
Contents lists available at SciVerse ScienceDirect
Energy and Buildings journal homepage: www.elsevier.com/locate/enbuild
Investigation on the influencing factors of energy consumption and thermal comfort for a passive solar house with water thermal storage wall Weiliang Wang, Zhe Tian, Yan Ding ∗ School of Environmental Science and Engineering, Tianjin University, Tianjin 300072, China
a r t i c l e
i n f o
Article history: Received 7 September 2012 Received in revised form 3 April 2013 Accepted 4 May 2013 Keywords: Passive solar house Water thermal storage wall Orthogonal analysis Variance analysis
a b s t r a c t A passive solar house (PSH) could fully receive and store the incident radiation by the rational arrangement of the building structure and the utility of the massive construction materials. The influence of water thermal storage wall (WTSW) on the indoor thermal environment is analyzed in this paper. Besides, different parts of the building envelope exert varying degrees of impact on the building energy consumption and indoor thermal comfort. Research on the influencing factors could provide references for the building energy conservation design and the retrofit of existing buildings. A PSH with interior walls of WTSW is studied, which is currently being used in North China. Field measurements were carried out in the reference building. TRNSYS was used to simulate the variation of indoor air temperature. The results of simulation and orthogonal analysis indicate that compared to traditional wall the PSH equipped with WTSW can reduce yearly energy consumption by 8.6% and improve indoor thermal comfort evaluation index by 12.9%. Meanwhile, significance of four different structural parameters (namely the shape coefficient, building orientation, glazing ratio of the south wall and the interior wall structure) respectively on the energy consumption and thermal comfort is obtained by the variance analysis. © 2013 Elsevier B.V. All rights reserved.
1. Introduction Building energy consumption in European countries accounts for 40% of the total social energy use [1], while in New York reaches up to 2/3 [2]. Since building energy consumption is able to be reduced significantly by fully consideration of the heating and cooling loads at building design stage, much more attentions are paid on the optimization of the building envelopes than ever before [3]. The influencing factors of the building envelope generally include the outline dimension, orientation, glazing area and thermo-physical properties of construction materials [4]. Heat exchange area with the outdoor air is determined by the building outline dimension. Wang et al. [5] researched the impact of flat shape on the building life cycle cost. Ourghi et al. [6] considered that a strong correlation existed between the yearly energy consumption and the building shape coefficient, which was defined as the ratio of external surface area over the inner volume. In addition, Depecker et al. [7] regarded the above strong correlation to be effective only in the places with large heating degree days or a short duration of sunshine. Besides, Marks [8] and Adamski [9] optimized the outline dimension from the building costs and yearly
∗ Corresponding author. Tel.: +86 13821601196; fax: +86 02287891898. E-mail address: [email protected] (Y. Ding). 0378-7788/$ – see front matter © 2013 Elsevier B.V. All rights reserved. http://dx.doi.org/10.1016/j.enbuild.2013.05.007
heating costs. Mingfang [10] studied the optimal building form basing on the solar heat control in summer and Aksoy et al. [11] researched the influence of shape factor on the heating demand in winter. Solar energy received by building depends on its orientation and glazing area. Morrissey et al. [12] considered that buildings with high energy efficiency normally owned a wide variation range in orientation. Researches of Shaviv [13] and Florides et al. [14] showed that the wall with a maximum glazing ratio (namely the ratio of window to wall) or with a longer edge should face south in hot and humid areas. Littlefair [15] considered that the optimal orientation can have an angle of less than 20–30◦ with due south. As to the glazing area, Persson et al. [16] found an appropriate glazing ratio after the optimization on the window. While in the opinion of Leskovar et al. [17,18], the optimal glazing ratio was determined by the heat transfer performance of the building envelope. Properties of building material directly influence the heat transfer and heat storage capacity of the wall. Oral et al. [19] introduced a method to determine the limit value of the wall heat transfer coefficient. Lollini et al. [20] and Jinghua et al. [21] studied the influence of the wall thermal insulation on the energy consumption. And C¸omakli et al. [22] investigated the optimum insulation thickness of the exterior wall. Khalifa [23] analyzed the sensitivity of the indoor air temperature to the change in the thermal storage
W. Wang et al. / Energy and Buildings 64 (2013) 218–223
219
Fig. 1. Typical structure form of the Trombe wall.
wall. Hassanain et al. [24] researched the passive heating effect of heat storage wall in passive solar houses with different roof shapes. While Khalifa et al. [25] and Gregory et al. [26] studied the massive wall with different construction material. Trombe wall is an effective form to exploit the thermal storage capacity. The related researches were conducted in the thermal performance comparison with transparent modular wall [27], the comparison of different types of Trombe wall [28], the winter passive heating effect [29], the summer passive cooling effect [30] and the reduction in energy consumption and CO2 emissions [31]. Typical structure form of the Trombe wall is shown as Fig. 1 [32]. Kinds of materials are suitable for filling in the massive wall in Fig. 1, one of which is water [33]. During construction process, water thermal storage wall (WTSW) is usually built by enclosing the water with concrete [34] or stainless steel plate. There are little literatures concerning on this kind of high thermal capacity but low-cost wall. Moreover, only single influencing factor of the building envelope was concerned in the previous researches and a comprehensive evaluation is in great need. Thus, a case study was conducted on a passive solar house (PSH) with WTSW in North China. A simulation model is also established with TRNSYS in order to study the influence of WTSW on the indoor thermal environment as well as the energy consumption. Orthogonal analysis is designed to compare the influencing significance of four different structural parameters, including the shape coefficient, building orientation, glazing ratio of the south wall and the interior wall structure, with the help of variance analysis. 2. Description of the reference building 2.1. Introduction of the passive solar house with water thermal storage wall The single-storey passive solar house faces due south, which has a floor area of 700 m2 and a shape coefficient (the ratio of external surface area to inner volume) of 0.374. It is located in Tianjin, North China. The house is divided into two zones, which can be seen in Fig. 2. A sun space corridor with a width of 1.4 m lies on the outside margin of the south zone, which can receive solar radiation as much as possible with a glazing ration of 100% in the south wall. The WTSW, outer wall made of steel plate, is processed into modular production with a dimension of 1.1 m × 0.4 m × 2.5 m (length × width × height). WTSW modules are placed along the inner side of the corridor besides some of the interior walls, which are shown by the shaded portion in Fig. 2. Total 29 modules are used in the PSH to store the received solar heat during the daytime. External and other interior walls consist of the steel construction and gypsum boards with a total thickness of 60 mm. 2.2. Simulation and validation of the reference building model According to the description, a building model was established in TRNSYS to simulate the hourly air temperature in different zones.
Fig. 2. Floor plan of the passive solar house with water thermal storage wall.
Weather data from the Tianjin Meteorological Bureau was adopted in the simulation. In order to validate simulation results and evaluate the effect of the WTSW, the indoor air temperature has been continuously monitored for more than one week from November 18th to 27th. Four temperature data loggers were fixed at point A–D as shown in Fig. 2. The average temperature of different zones by simulation and field test in the PSH are shown respectively in Fig. 3. In the condition of no space heating, the measured indoor temperature remained at 13.7 ◦ C mainly because of the high thermal capacity of the WTSWs even when the outside air dropped to −0.4 ◦ C (7:00 am November 23rd). The simulated temperature had a maximum relative error of 24.5% compared to the measured values. Such error is probably related with the randomness of human’s activity, such as opening or closing the door, turning on or off the lights and so on. An average relative error of 8.97% reflects the accuracy of the simulation, so this model is suitable for the later orthogonal analysis. 3. Orthogonal analysis 3.1. Test factors and levels Based on the literature review, four structural parameters are chosen as the test factors, namely the shape coefficient (SC), building orientation (BO), glazing ratio of the south wall (GR) and interior wall structure (IS). The low levels of each factor are the real dimensions of the reference building in North China, while the high levels are the limitations regulated in design standards of energy efficient residential buildings. The medium levels are the average values of the low levels and high levels. All the factor levels are listed in Table 1. Among which, the realistic orientation barely has an angle of more than 30◦ with due south, and the levels of glazing ratio and shaping coefficient are determined by referring to the energy saving design standard [35] and the original values of the reference building. Besides, the ordinary wall with a layout illustrated in Table 2 is a popular form used in practice, which can meet the requirement of the energy saving standard.
220
W. Wang et al. / Energy and Buildings 64 (2013) 218–223
Fig. 3. Comparison results of the simulating and measuring temperature.
Table 1 Test factors and the corresponding levels. Levels
1 2 3
Factors SC Shape coefficient
BO Building orientation
GR Glazing ratio of south wall
IS Interior wall structure
0.374 0.437 0.52
−30◦ (Southwest) 0◦ (Due south) 30◦ (Southeast)
100% 75% 50%
Ordinary wall WTSW –
3.2. Introduction of the test index The energy consumption and thermal comfort are studied respectively by orthogonal analysis. 3.2.1. New degree days Human body can adapt to a changing environment. Humphreys and Nicol [36] proposed an adaptive model as shown in Eq. (1) for the indoor thermal comfort temperature tcomf , where tout is the outdoor monthly average temperature. While de Dear and Brager [37] simplified the model under the condition of no space heating or cooling. The simplified model is described as Eq. (2). The dissatisfied percentage will be less than 20% even if the indoor temperature varies between tcomf ± 3.5 ◦ C [37], thus the range of tcomf can be determined.
t − 22 2 out
tcomf = 24.2 + 0.43(tout − 22) exp −
√ 24 2
tcomf = 0.31tout + 17.8
(1) (2)
Degree days (DDs) are widely used to measure the energy consumption in different outside conditions [38]. In this study, new degree days (NDDs), which are the hourly accumulation of the absolute difference between the indoor temperature tair and the limits of tcomf during a year as described in Eq. (3), are used to measure the yearly energy consumption under different indoor conditions. Because the thermal capacity of the building enve-
lope may change when the test factors take different levels, NDDs become an appropriate index of the building energy consumption due to the independence of the heat capacity.
NDDs =
⎧
[tair − (tcomf + 3.5)], tair > tcomf + 3.5 ⎪ ⎨ 0,
⎪ ⎩
tcomf − 3.5 < tair < tcomf + 3.5
[(tcomf − 3.5) − tair ],
(3)
tair < tcomf − 3.5
3.2.2. Discomfort index Thermal comfort sense is influenced by the steady state and dynamic factors. The former refers to the absolute deviation of the effective temperature and the optimum temperature, while the latter represents the relative change rate of the absolute deviation upon the time. These two factors are fully considered in the discomfort index (DI), which is shown in Eqs. (4)–(6) [39]. DI =
MSE + 5MSR 6
MSE =
m =1
MSR =
E2 =
m
(4)
(ET − PT)2
(5)
=1
m E − E−1 =1
(6)
2
Table 2 Layout of the ordinary wall. Materials
Density (kg/m3 )
Conductivity (W/(m K))
Capacity (J/(kg K))
Thickness (m)
Cement mortar Concrete hollow block Insulation mortar
1800 1300 250
0.93 0.68 0.06
1050 538 1050
0.02 0.2 0.02
W. Wang et al. / Energy and Buildings 64 (2013) 218–223
221
Table 3 Orthogonal analysis table and the calculation results of the test indexes. Test no.
Factors
1 2 3 4 5 6 7 8 9
Test indexes
SC Shape coefficient
BO Orientation
GR Glazing ratio
IS Interior wall structure
NDDs ◦ C·days
DI –
1 1 1 2 2 2 3 3 3
1 2 3 1 2 3 1 2 3
1 2 3 2 3 1 3 1 2
1 2 3 3 1 2 2 3 1
20146.7 17874.7 24026.6 24220.2 25487.3 16535.1 28060.7 19672.8 23140.2
23,172 18,381 29,516 31,462 32,970 16,121 38,837 22,363 28,070
where, MSE and MSR represent the steady state factor and dynamic factor, and m mean the current hour and total hours, ET is the effective temperature, PT is the optimum temperature and E is the absolute deviation of ET and PT. The constant optimum temperature of 16 ◦ C and 14 ◦ C were recommended for the daytime and night time separately [39], which ignored the physically adjusting ability of the human body. Thus, in this paper, the lower and upper limits of tcomf are chosen for PT in winter and summer respectively. 4. Results and discussion 4.1. Orthogonal analysis table and results According to the test factors and levels, the L18 (21 × 37 ) orthogonal arrays are originally required mostly because factor IS has only two levels. The orthogonal analysis could be optimized by the quasi level method. Specifically, a pseudo level of IS3, which is chosen the same as IS1 of the ordinary wall, is added for factor IS. Thus the L9 (34 ) orthogonal analysis table can be used as shown in Table 3. Although reducing the simulation work largely, the new arrays do not have the orthogonality any more because of the same two levels of factor IS. But special handling on the variance analysis in Section 4.2.2 can ensure the feasibility of the significance study [40]. Totally 9 groups of conditions are simulated and the results of those two test indexes are listed in Table 3. 4.2. Analysis on the orthogonal analysis results 4.2.1. The influence of WTSW on energy use and thermal comfort index The influence of the new type of thermal storage wall is studied comparing to the ordinary wall. Both IS1 and IS3 represent the ordinary wall in Table 3, while IS2 represent the thermal storage wall. The index results of factor IS with the same level in Table 3 are averaged and then listed in Table 4. From which, the WTSW surpasses the ordinary wall which is widely used in China both from
Table 4 Influence of WTSW on energy use and thermal comfort index. Levels of factor IS
Implication
NDDs ◦ C·days
DI –
IS1 & IS3 IS2
Ordinary wall WTSW
22782.3 20823.5
28,070 24,446
the energy saving and thermal comfort aspects. For the NDDs and DI represent the annually heating and cooling energy consumption and the thermal discomfort level respectively, the quantitative results in Table 4 indicate that the PSH with WTSW can cut 8.6% of the yearly energy consumption while raise the comfort evaluation index by 12.9%. 4.2.2. Variance analysis of the test factors Variance analysis is carried out as below. Firstly, the sum of deviation square (SS) is discomposed by the degree of freedom (DF) into mean squares (MS). Statistical variables of F-test are constructed by the mean square ratio of the different factors and the error. Then the significance of factors can be obtained. The calculation method of each item in the variance analysis table is listed in Appendix. Among which, the SS of factor IS is calculated in the same formula with other factors except that the level amount of k should takes the actual amount (kD = 2 in this paper). Thus, the test numbers of factor IS in level 1 and 2 are 6 and 3 respectively, and the corresponding DF equals 1. The variance analysis of NDDs and DI are respectively shown in Tables 5 and 6. In the variance analysis table, F-value reflects the impact of a factor compared with that of the error on the test index. Since the influence of the error is generally small, the more the F-value is close to 1, the less significance of the factor will be. According to the variance analysis results, the order of the significance degree can be obtained as following. With regard to the influence on the energy consumption, the order is GR (glazing ratio) > BO (building orientation) > IS (interior wall structure) > SC (shape coefficient). As to the thermal comfort, the order is GR (glazing ratio) > BO (building orientation) > SC (shape coefficient) > IS (interior wall structure). So
Table 5 Variance analysis table with index of NDDs. Items
SS
DF
MS
SC BO GR IS Error Sum
12992101 18312102 75713108 7673988 121732 114813031
2 2 2 1 1 8
6496050 53.4 49.5 9156051 75.2 49.5 37856554 310.9 49.5 7673988 63.0 39.9 121732 F0.1 (2,1) = 49.5, F0.05 (2,1) = 199.5, F0.01 (2,1) = 4999.5 F0.1 (1,1) = 39.9, F0.05 (1,1) = 161.4, F0.01 (1,1) = 4052.2
F-value
Critical value of F-value 0.1
* **
The significance level between 0.1 and 0.05, which can be deemed as a moderately significant influencing factor. The significance level between 0.05 and 0.01, which can be deemed as a highly significant influencing factor.
Significance
0.05
0.01
199.5 199.5 199.5 161.4
4999.5 4999.5 4999.5 4052.2
* * ** *
222
W. Wang et al. / Energy and Buildings 64 (2013) 218–223
Table 6 Variance analysis table with index of DI. Items
SS
DF
MS
F-value
Critical value of F-value 0.1
SC BO GR IS Error Sum *
55252873 86776473 265099313 24209274 126025.08 431463957
2 2 2 1 1 8
27626437 219.2 49.5 43388236 344.3 49.5 132549656 1051.8 49.5 24209274 192.1 39.9 126025.1 F0.1 (2,1) = 49.5, F0.05 (2,1) = 199.5, F0.01 (2,1) = 4999.5 F0.1 (1,1) = 39.9, F0.05 (1,1) = 161.4, F0.01 (1,1) = 4052.2
Significance
0.05
0.01
199.5 199.5 199.5 161.4
4999.5 4999.5 4999.5 4052.2
** ** ** **
The significance level between 0.1 and 0.05, which can be deemed as a moderately significant influencing factor. ** The significance level between 0.05 and 0.01, which can be deemed as a highly significant influencing factor.
both from the view of energy saving and thermal comfort, the glazing ratio and building orientation are the two most important parts for designers to focus on. Besides, even the water thermal storage wall owns large benefits compared to the ordinary walls, the interior wall structure is not the most important factor among those four structure parameters.
5. Conclusions With the increasingly large amount of the building energy consumption, optimization design on the building envelope becomes an effective method to reduce the building energy demand. This paper studied a passive solar house in use that comprised a new kind of water thermal storage wall. The reference building model was established with TRNSYS. Basing on the model, the orthogonal analysis was designed to study the influence of the WTSW and the impact significance degrees of different structure parameters. Conclusions are obtained as following. (1) The indoor environment with WTSW is better than that with ordinary wall. The superiority arises both from the view of energy saving and the thermal comfort. The studied PSH with WTSW reduce 8.6% of the yearly energy consumption and raise the thermal comfort evaluation index by 12.9%. (2) With regard to the energy consumption index of NDDs, the influence significance order of the building envelope parameters is the glazing ratio, building orientation, the interior wall structure and shape coefficient. (3) As to the thermal comfort index of DI, which indicates the thermal discomfort, the influence significance order becomes the glazing ratio, building orientation, shape coefficient and the interior wall structure.
Acknowledgments This research has been supported by National Science and Technology Support Program (Grant No. 2011BAJ03B00).
Appendix A.
⎛ n ⎜ ⎜ ⎜yi − SST = ⎜ i=1 ⎝
DFT = n − 1
n i=1
n
⎞2 yi ⎟
⎟ ⎟ ⎟ ⎠
(A.1)
(A.2)
⎛
kfactor
SSfactor =
i=1
⎜ ⎜ ni ⎜ ⎜Ti − ⎝
n i=1
n
⎞2 yi ⎟
⎟ ⎟ ⎟ ⎠
(A.3)
DFfactor = kfactor − 1
(A.4)
SSe = SST −
(A.5)
DFe = DFT −
SSfactor
DFfactor
(A.6)
MS = SS/DF
(A.7)
Ffactor = MSfactor /MSe
(A.8)
where SS is the sum of deviation square, DF is the degree of freedom, MS is the mean square, F is the statistical variable of F-test. As to the subscript, T means total, factor includes test factor of SC, BO, GR and IS. e is the error. In the formula, yi is the test result of the group i, n is the total number of test groups, kfactor is the level number of a factor, ni is number of test groups in level i, T¯ i is the average test results in level i. References [1] H.-X. Zhao, F. Magoulès, A review on the prediction of building energy consumption, Renewable and Sustainable Energy Reviews 16 (6) (2012) 3586–3592. [2] B. Howard, L. Parshall, J. Thompson, S. Hammer, J. Dickinson, V. Modi, Spatial distribution of urban building energy consumption by end use, Energy and Buildings 45 (2012) 141–151. [3] R. Pacheco, J. Ordo˜nez, G. Martinez, Energy efficient design of building: a review, Renewable and Sustainable Energy Reviews 16 (2012) 3559–3573. [4] B.B. Ekici, U.T. Aksoy, Prediction of building energy needs in early stage of design by using ANFIS, Expert Systems with Applications 38 (2011) 5352–5358. [5] W. Wang, H. Rivard, R. Zmeureanu, Floor shape optimization for green building design, Advanced Engineering Informatics 20 (2006) 363–378. [6] R. Ourghi, A. Al-Anzi, M. Krarti, A simplified analysis method to predict the impact of shape on annual energy use for office buildings, Energy Conversion and Management 48 (2007) 300–305. [7] P. Depecker, C. Menezo, J. Virgone, S. Lepers, Design of buildings shape and energetic consumption, Building and Environment 36 (2001) 627–635. [8] W. Marks, Multicriteria optimisation of shape of energy-saving buildings, Building and Environment 32 (4) (1997) 331–339. [9] M. Adamski, Optimization of the form of a building on an oval base, Building and Environment 42 (2007) 1632–1643. [10] T. Mingfang, Solar control for buildings, Building and Environment 37 (2002) 659–664. [11] U.T. Aksoy, M. Inalli, Impacts of some building passive design parameters on heating demand for a cold region, Building and Environment 41 (2006) 1742–1754. [12] J. Morrissey, T. Moore, R.E. Horne, Affordable passive solar design in a temperate climate: an experiment in residential building orientation, Renewable Energy 36 (2011) 568–577. [13] E. Shaviv, The influence of the orientation of the main solar glazing on the total energy consumption of a building, Solar Energy 26 (1981) 453–454. [14] G.A. Florides, S.A. Tassou, S.A. Kalogirou, L.C. Wrobel, Measures used to lower building energy consumption and their cost effectiveness, Applied Energy 73 (2002) 299–328.
W. Wang et al. / Energy and Buildings 64 (2013) 218–223 [15] P. Littlefair, Daylight, sunlight and solar gain in the urban environment, Solar Energy 70 (3) (2001) 177–185. [16] M.-L. Persson, A. Roos, M. Wall, Influence of window size on the energy balance of low energy houses, Energy and Buildings 38 (3) (2006) 181–188. [17] V.Z. Leskovar, M. Premrov, Influence of glazing size on energy efficiency of timber-frame buildings, Construction and Building Materials 30 (2012) 92–99. [18] V.Zˇ . Leskovar, M. Premrov, An approach in architectural design of energyefficient timber buildings with a focus on the optimal glazing size in the south-oriented fac¸ade, Energy and Buildings 43 (12) (2011) 3410–3418. [19] G.K. Oral, Z. Yilmaz, The limit U values for building envelope related to building form in temperate and cold climatic zones, Building and Environment 37 (2002) 1173–1180. [20] Lollini, Barozzi, Fasano, Meroni, Zinzi, Optimisation of opaque components of the building envelope energy, economic and environmental issues, Building and Environment 41 (2006) 1001–1013. [21] Y. Jinghua, Y. Changzhi, T. Liwei, Low-energy envelope design of residential building in hot summer and cold winter zone in China, Energy and Buildings 40 (2008) 1536–1546. [22] K. C¸omakli, B. Yüksel, Optimum insulation thickness of external walls for energy saving, Applied Thermal Engineering 23 (2003) 473–479. [23] A.-J.N. Khalifa, A study on a passive heated single-zone building using a thermal storage wall, Energy Conversion and Management 39 (1998) 1597–1605. [24] A.A. Hassanain, E.M. Hokam, T.K. Mallick, Effect of solar storage wall on the passive solar heating constructions, Energy and Buildings 43 (2/3) (2011) 737–747. [25] A.J.N. Khalifa, E.F. Abbas, A comparative performance study of some thermal storage materials used for solar space heating, Energy and Buildings 41 (4) (2009) 407–415. [26] K. Gregory, B. Moghtaderi, H. Sugo, A. Page, Effect of thermal mass on the thermal performance of various Australian residential constructions systems, Energy and Buildings 40 (2008) 459–465.
223
[27] J.K. Nayak, Transwall versus Trombe wall: relative performance studies, Energy Conversion and Management 27 (4) (1987) 389–393. [28] J. Shen, S. Lassue, L. Zalewski, D. Huang, Numerical study on thermal behavior of classical or composite Trombe solar walls Classical Trombe wall, Energy and Buildings 39 (2007) 962–974. [29] H. Onbasioglu, A.N. Egrican, Experimental approach to the thermal response of passive systems, Energy Conversion and Management 43 (2002) 2053–2065. [30] G. Gan, A parametric study of Trombe wall for passive cooling of buildings, Energy and Buildings 27 (1998) 37–43. [31] A. Chel, J.K. Nayak, G. Kaushik, Energy conservation in honey storage building using Trombe wall, Energy and Buildings 40 (9) (2008) 1643–1650. [32] S. Jaber, S. Ajib, Optimum design of Trombe wall system in mediterranean region, Solar Energy 85 (9) (2011) 1891–1898. [33] G.N. Tiwari, Y.P. Yadav, S.A. Lawrence, Performance of a solarium: an analytical study, Building and Environment 23 (2) (1988) 145–151. [34] S.C. Kaushik, S. Kaul, Thermal comfort in buildings through a mixed water-mass thermal storage wall, Building and Environment 24 (1989) 199–207. [35] GB50189-2005 Design Standard for Energy Efficiency of Public Buildings, China Architecture and Building Press, Beijing, 2005 (in Chinese). [36] J.F. Nicol, M.A. Humphreys, Adaptive thermal comfort and sustainable thermal standards for buildings, Energy and Buildings 34 (2002) 563–572. [37] R.J. de Dear, G.S. Brager, Thermal comfort in naturally ventilated buildings: revisions to ASHRAE Standard 55, Energy and Buildings 34 (2002) 549–561. [38] G.J. Schoenau, R.A. Kehrig, Method for calculating degree-days to any base temperature, Energy and Buildings 14 (4) (1990) 299–302. [39] Y.-Z. Li, Passive Solar House Thermo-technical Design Manual, Tsinghua University Press, Beijing, 1993 (in Chinese). [40] X.-M. Zhao, Experimental Design Methods, Science Press, Beijing, 2010 (in Chinese).