analysis of the influences of solar radiation and façade glazing ...

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1.2 Background and literature review 16 Chen (Zhai, et al., 2002) (Zhai, et al., 2003) (Zhai, et al., 2005) (Zhai, et al., 2006). They proposed different static, dynamic and bin coupling strategies to decrease the computing time. A new coupling building energy simulation tool was developed and validated with experimental data available in literature. It was found that the best efficient coupling method is a transfer of surface temperatures from the energy simulation code to a CFD preprocessor. After calculation, heat transfer coefficients and gradients of air temperature are returned in the opposite direction. In order to reduce CPU-time demands, Zhai and Chen proposed the optimal staged coupling strategy. The European Joule–Thermie OFFICE project concerned with labeling buildings checked the compliance of a building with regulations and evaluated the efficiency of the retrofit. Within this research project framework Roulet with colleagues (Roulet, et al., 2002) developed multi-criteria procedures based on a principle component analysis and on the ELECTRE family partial aggregation method. The proposed methodology can be used both before and after the retrofit. Energy management and control units can monitor and optimize the work of various HVAC components during operation. Salsbury and Diamond (Salsbury, et al., 2000) created the concept of using simulation in the validation and energy analysis of HVAC systems in buildings. In this conception, a complex system is composed of a number of several linked subsystem models. The potential of using a simulation, which represents virtual and real parallel operating systems, was seen in a dual-duct air-handling unit located in an office building in San Francisco. Calculations were performed in the MATLAB programming environment. It was indicated that the use of simplified models can decrease the number of configuration parameters in a simulation. Building energy performance can be predicted based on an artificial neural networks (ANN) method. Yezioro and co-workers (Yezioro, et al., 2008) developed and tested ANN using data from one week of an experimental period. The Pittsburgh Synergy Solar House was selected as the reference building. The experimental database consisted of the following electricity consumption: total, lighting, HVAC and electricity generated in the photovoltaic system. The MATLAB environment was used to implement the considered model of ANN. Calculation results from four building performance simulation tools: Energy_10, Green Building Studio, eQuest and EnergyPlus were used for the comparison of ANN purposes. It presented a good correlation (mean absolute error equal to 0.9 %) between the predictions and the results from the mathematical model. Reducing the number of tests for complex systems can be done by the use of a lattice method for global optimization (LMGO) which was developed by Saporito (Saporito, et al., 2001). The influence of different design parameters of building energy consumption was investigated. In order to identify the main energy saving features, simulations of thermal behavior in simple office buildings located in Kew (London) with help of APACHE code were performed. The authors concluded that LMGO can be successfully
1.2 Background and literature review 17 used in both sensitivity studies of dynamic systems and in building optimization problems with a large number of combination tests. Building structures and environments are modeled by a system of differential algebraic equations. Required smoothness assumptions that can be applied in the solution of these types of equation sets have been proposed by Wetter (Wetter, 2005). A new multi-zone building energy simulation program called BuildOpt, which differs from other software because of the inclusion of various smoothing algorithms, was presented. The numerical experiments indicated a reduction in the computation time and a high precision of smoothing techniques proposed by the author. Multi-objective genetic algorithm (MOGA) was used by Wright (Wright, et al., 2002) to estimate the optimum pay-off characteristic between daily energy costs and the quality of the thermal environment in the building. An example of a single zone HVAC system composed of cooling and heating coils, a regenerative heat exchanger and a supply fan was used to show the benefits of the multi-criterion optimization genetic algorithm. Estimates indicated that MOGA search methods can be successfully used in the thermal design of buildings in respect to occupant comfort. Genetic algorithms were used by Xu and Wang (Xu, et al., 2007) in the thermal modelling of the building envelope. They developed a method to optimize the parameters of the simplified dynamic model based on frequency domain regression. Validation of the optimization method and its effectiveness were conducted by comparing the predictions with the results from the theoretical model. It was found that the frequency domain analysis greatly simplified the search for optimal parameters. Earth-contact heat transfers in built environments were investigated by Davies and colleagues (Davies, et al., 2001). They improved the efficiency of the numerical technique by adopting some elements from the response factor method. The results of calculations based on the new model showed a dramatic decrease in the computing time of the simulations compared to the traditional finite volume technique in keeping with accuracy and flexibility. The accuracy of the building energy simulations strongly depends on the estimate of solar irradiance on external facades. Loutzenhiser, along with co-workers (Loutzenhiser, et al., 2007), validated short-wave radiation in solar gain models applied in energy simulation software. In the experiment, a database of solar radiation from two 25-day measurements performed on the EMPA campus located in Duebendorf (Switzerland) was used. Calculations were made using four building energy simulation programs: EnergyPlus, DOE-2.1e, ESP-r and TRNSYS-TUD and seven solar radiation models. Using the mean absolute differences method, it verified that the uncertainties of the models are as follows: 14.9 % for the isotropic sky, 9.1 % for the Hay–Davies, 9.4 % for the Reindl, 7.6 % for the Muneer, 13.2 % for the Klucher, 9.0 % for the modified Perez and 7.9 % for Perez.
Page 1 and 2: ANALYSIS OF THE INFLUENCES OF SOLAR
Page 3 and 4: than the inner air temperature. It
Page 5 and 6: 4.1 SUMMARY .......................
Page 7 and 8: Subscripts σ - Stefan-Boltzmann co
Page 9 and 10: 1.1 Introduction 9 Another problem,
Page 11 and 12: 1.2 Background and literature revie
Page 15: 1.2 Background and literature revie
Page 41 and 42: 2.1 Building energy simulation soft
Page 47 and 48: 2.2 Experimental research methods 4
Page 53 and 54: 3.1 Building description 53 Fig. 3.
Page 55 and 56: 3.1 Building description 55 All int
Page 57 and 58: 3.1 Building description 57 Case B5
Page 59 and 60: 3.1 Building description 59 Of cour
Page 61 and 62: 3.1 Building description 61 Outside
Page 63 and 64: 3.1 Building description 63 In orde
Page 65 and 66: 3.1 Building description 65 Fig. 3.
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3.1 Building description 67 Fig. 3.
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3.2 Selection of the optimal glazin
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3.3 Estimation of thermal energy ga
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3.4 Testing of a building indoor en
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3,00 2,50 2,00 1,50 1,00 0,50 0,00
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3.5 Optimization of a solar domesti
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4.1 Summary 99 4 Summary and conclu
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4.2 Comments and conclusions 101 So
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4.3 Future research 103 of the annu
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References 105 Beausoleil-Morrison,
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References 107 Hendron, R., et al.
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References 109 Rees, S. and Haves,
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References 111 Yohanis, Y.G., et al
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Appendix 1 113 Fig. A1.1 VASATI 2.0
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Appendix 2 123 !-Generator IDFEdito
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Appendix 2 125 0.035, !- Conductivi
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Appendix 2 127 , !- Zone Inside Con
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Appendix 2 129 Discrete; !- Numeric
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Appendix 2 131 Fraction, !- Schedul
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Appendix 2 133 Air Conditioner:Wind
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Appendix 2 135 , !- Sensible Heat F
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Appendix 2 137 281, !- Lighting Lev
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Appendix 2 139 OG2 InfilTRATION, !-
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Appendix 2 141 OG3, !- Zone Name 16
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Appendix 2 143 0.004, !- Zone Heati
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Appendix 2 145 Zone15WindACAirInlet
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Appendix 2 147 0, !- Maximum Flow R
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Appendix 2 149 Zone16WindACOAMixer,
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Appendix 2 151 Zone 9 Outlet Node;
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Appendix 2 153 Stand Alone ERV 16,
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Appendix 2 155 ZoneHVAC:WindowAirCo
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Appendix 2 157 ZoneHVAC:Baseboard:C
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Appendix 2 159 Zone Control Type Sc
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Appendix 2 161 WindACPLFFPLR, !- Pa
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Appendix 2 163 Stand Alone ERV Supp
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Appendix 2 165 0.03001, !- Maximum
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Appendix 2 167 0.81, !- Sensible Ef
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Appendix 2 169 !- == ALL OBJECTS IN
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