Advanced Building Simulation

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References Simulation and uncertainty: weather predictions 85 Adelard, L., Thierry, M., Boyer, H., and Gatina, J.C. (1999).“Elaboration of a new tool for weather data sequences generation.” In Proceedings of Building Simulation ’99, Vol. 2, pp. 861–868. ASHRAE (2001). Handbook of Fundamentals—2001. American Society of Heating, Refrigerating and Air-Conditioning Engineers, Inc., Atlanta, GA, Chap. 30, p. 30.13. Crow, Loren W. (1983). “Development of hourly data for weather year for energy calculations (WYEC), including solar data for 24 stations throughout the United States and five stations in southern Canada.” Report LWC #281, ASHRAE Research Project 364-RP, Loren W. Crow Consultants, Inc., Denver, CO, November. Crow, Loren W. (1984). “Weather year for energy calculations.” ASHRAE Journal, Vol. 26, No. 6, pp. 42–47. Degelman, L.O. (1970). “Monte Carlo simulation of solar radiation and dry-bulb temperatures for air conditioning purposes.” In Proceedings of the Kentucky Workshop on Computer Applications to Environmental Design. Lexington, KY, April, pp. 213–223. Degelman, L.O. (1976). “A weather simulation model for annual energy analysis in buildings.” ASHRAE Trans., Vol. 82, Part 2, 15, pp. 435–447. Degelman, L.O. (1981). “Energy calculation sensitivity to simulated weather data compression.” ASHRAE Trans, Vol. 87, Part 1, January, pp. 907–922. Degelman, L.O. (1990). “ENERCALC: a weather and building energy simulation model using fast hour-by-hour algorithms.” In Proceedings of 4th National Conference on Microcomputer Applications in Energy. University of Arizona, Tucson, AZ, April. Degelman, L.O. (1997). “Examination of the concept of using ‘typical-week’ weather data for simulation of annualized energy use in buildings.” In Proceedings of Building Simulation ’97, Vol. II, International Building Performance Simulation Association (IBPSA). 8–10 September, pp. 277–284. Haberl, J., Bronson, D., and O’Neal, D. (1995). “Impact of using measured weather data vs. TMY weather data in a DOE-2 simulation.” ASHRAE Trans., Vol. 105, Part 2, June, pp. 558–576. Huang, J. and Crawley, D. (1996). “Does it matter which weather data you use in energy simulations.” In Proceedings of 1996 ACEEE Summer Study. Vol. 4, pp. 4.183–4.192. Knapp, Connie L., Stoffel, Thomas L., and Whitaker, Stephen D. (1980). “Insolation data manual: long-term monthly averages of solar radiation, temperature, degree-days and global K T for 248 National Weather Service Stations,” SERI, SP-755-789, Solar Energy Research Institute, Golden, CO, 282 pp. Liu, Benjamin Y.M. and Jordan, Richard C. (1960). “The interrelationship and characteristic distribution of direct, diffuse and total solar radiation,” Solar Energy, Vol. IV, No. 3, pp. 1–13. NSRDB (1995). “Final Technical Report—National Solar Radiation Data Base (1961–1990),” NSRB-Volume 2, National Renewable Energy Laboratory, Golden, CO, January, 290 pp. Perez, R., Ineichen, P., Seals, R., and Zelenka, A. (1990). “Making full use of the clearness index for parameterizing hourly insolation conditions.” Solar Energy, Vol. 45, No. 2, pp. 111–114. Perez, Ineichen P., Maxwell, E., Seals, F., and Zelenda, A. (1991). “Dynamic models for hourly global-to-direct irradiance conversion.” In Proceedings of the 1991 Solar World Congress. International Solar Energy Society, Vol. I, Part II, pp. 951–956. Stoffel, T.L. (1993). “Production of the weather year for energy calculations version 2 (WYEC2).” NREL TP-463-20819, National Renewable Laboratory. Threlkeld, J.L. (1962). “Solar irradiation of surfaces on clear days.” ASHRAE Journal, Nov., pp. 43–54.
86 Degelman TRY (1976). “Tape reference manual—Test Reference Year (TRY).” Tape deck 9706, Federal Energy Administration (FEA), ASHRAE, National Bureau of Standards (NBS), and the National Oceanic and Atmospheric Administration (NOAA), National Climatic Center, Asheville, NC, September. TMY (1981). “Typical Meteorological Year (TMY) User’s Manual.” TD-9734, National Climatic Center, Asheville, NC, May, 57 pp. TMY2 (1995). “User’s Manual for TMY2s—Typical Meteorological Years.” NREL/SP-463- 7668, National Renewable Energy Laboratory, Golden, CO, June, 47 pp.
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Advanced Building Simulation
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First published 2003 by Spon Press
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vi Contents 8 Developments in inter
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viii Figures 3.12 Relation between
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x Figures 8.14 Workflow Modeling Wi
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Contributors D. Michelle Addington,
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Prologue Introduction and overview
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Prologue 3 two topics that represen
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Trends in building simulation 5 com
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Reality Space averaged treatment of
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Pervasive/invisible Web-enabled Des
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Trends in building simulation 11 in
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Trends in building simulation 13 ea
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Trends in building simulation 15 ba
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Trends in building simulation 17 1.
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Trends in building simulation 19 th
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and predictive simulation-based con
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Trends in building simulation 23 Fu
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Chapter 2 Uncertainty in building s
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Uncertainty in building simulation
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This completes the outline of the c
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and inputs may be a formidable task
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Kleijnen (1997), Reedijk (2000), an
Page 50 and 51: Table 2.1 Categories of uncertain m
Page 52 and 53: ∆C p (-) 1.6 1.2 0.8 0.4 0.0 -0.4
Page 54 and 55: Uncertainty in building simulation
Page 56 and 57: S d (h) 80 60 40 20 0 -80 2 Uncerta
Page 58 and 59: 2.4.2 Uncertainty in wind pressure
Page 60 and 61: an experiment involving structured
Page 66 and 67: with their uncertainties, in terms
Page 68 and 69: Table 2.5 Expected utilities for de
Page 70 and 71: y a performance gain on another asp
Page 76 and 77: Simulation and uncertainty: weather
Page 82 and 83: makes every month normalized to a z
Page 84 and 85: Dry-bulb temperature (°F) 60 50 40
Page 86 and 87: (b) Compute today’s average tempe
Page 90 and 91: ackward. First, the average daily h
Page 92 and 93: a��sin(�)* ln[I SC * � D] (
Page 94 and 95: H = Total daily horizontal insolati
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Page 98 and 99: Horizontal insolation (MJ/m 2 /day)
Page 102 and 103: Chapter 4 Integrated building airfl
Page 104 and 105: (a) (b) zone 6 5 4 3 2 1 3 1 Pre-he
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Page 108 and 109: West Zone n Zone m Figure 4.5 Examp
Page 110 and 111: Node n Figure 4.6 An example two zo
Page 112 and 113: The nodal pressures are then iterat
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Page 120 and 121: Air temperature (°C) 40.0 35.0 30.
Page 122 and 123: It was found that the differences a
Page 124 and 125: of the problem. In any event, calib
Page 126 and 127: Resolution CFD Decision Reduced Dec
Page 130 and 131: Although most of the basic physical
Page 134 and 135: Chapter 5 The use of Computational
Page 136 and 137: CFD tools for indoor environmental
Page 138 and 139: the Reynolds average rules can be s
Page 144 and 145: 3m Control room Enclosure 5.5 m Out
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Y/H Y/H 1 0.8 0.6 0.4 0.2 Plot-1 Pl
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magnitude computing time than the R
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CFD tools for indoor environmental
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Chapter 6 New perspectives on Compu
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New perspectives on CFD simulation
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law must be invariant to a transfor
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References New perspectives on CFD
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Chapter 7 Self-organizing models fo
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Self-organizing models for sentient
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SOM topo- SOM site 1 graphy 1 1 1 1
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DC EL1 DC EL2 MC EL_1 DC EL3 E 1 MC
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technologies (Wouters 1998; Mahdavi
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and photometric properties, as well
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exploratory implementations. The fi
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the electric light component of ill
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Chapter 8 Developments in interoper
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This chapter introduces the technol
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Developments in interoperability 19
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Instances subset A Building Model S
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Import DT data Export DT data DT sc
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Data-centric approach no explicit p
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Glazing system? Window area? Monday
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Chapter 9 Immersive building simula
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Immersive building simulation 219 T
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Immersive building simulation 221 E
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Data generation Data preparation Ma
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Shear Velocity Acceleration Curvatu
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Immersive building simulation 227 F
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etter than the others, they suffer
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quality CRT screens, wide field of
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Graphical representation Matrix (4D
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Figure 9.22 Test room—section. Wa
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(a) (b) Immersive building simulati
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Immersive building simulation 239 m
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Calibrated tracker data Figure 9.30
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Immersive building simulation 243 g
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Immersive building simulation 245 H
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Epilogue Godfried Augenbroe and Ali
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Index accountability 13 accreditati
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performance assessment methods 109-
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