Advanced Building Simulation

More documents

Recommendations

Info

Horizontal insolation (MJ/m 2 /day) 35 30 25 20 15 10 5 0 Jan <strong>Simulation</strong> and uncertainty: weather predictions 83 Actual historical data Simulated results Feb Mar Apr May Jun Jul Aug Sep Oct Nov Dec Figure 3.17 Comparison of horizontal daily solar radiation from simulated versus real data. Clg DD SD Clg DD Ave Htg DD SD Htg DD Ave Solar SD Solar Ave 6.1 6.3 0 20 19 19.5 105.2 103.8 118.8 117.4 128.3 127.7 141.4 142 Historical Simulated 40 60 80 100 120 140 160 Figure 3.18 Comparison of results from the simulation model to historical weather data. Values represent monthly mean (Ave) and standard deviation (SD) results for heating degreedays, cooling degree-days, and horizontal insolation. also showed less than a 2% difference in an office building’s annual energy consumption when driven by simulated weather data versus the actual weather data from a SOLMET data file of recorded data. The data in Figures 3.15–3.17 are plots of monthly degree-days and solar radiation as derived from the SOLMET file for Dallas, TX,
84 Degelman compared to the simulated weather data for the same period. It should be noted that the statistics used in the weather data simulator were actually derived from the same SOLMET data file. This indicates that the simulator is able to re-create at least the dry-bulb temperature and solar distribution with a high degree of accuracy when historical weather data statistics are available. Figure 3.18 compares the cumulative values from synthetically generated data to actual weather records for Dallas, TX. Shown are average monthly means and standard deviations for heating degree-days, cooling degree-days, and horizontal insolation values. 3.11 Finding input data for driving a statistical weather model Synthetically generating hourly weather data requires not only a reliable modeling tool, but also a good source of recorded weather statistics. One source of worldwide weather data is available from the National Climatic Data Center (NCDC) in Asheville, NC, USA. On-line access to their publications is possible on their internet site: http://www.ncdc.noaa.gov/oa/climate/climateproducts.html. One product (for sale) is a CD-ROM that contains the 1961–1990 global standard climate normals for over 4,000 stations worldwide, representing more than 135 countries and territories. This CD-ROM contains no software or extraction routines that allow users to import the data directly into their spreadsheets or other applications; however, the files can be read by software written by the user according to the format specifications outlined in the documentation files. The data files may also be opened by any ASCII-compatible application that can handle large data volumes. This NCDC product was produced in conjunction with the World Meteorological Organization (WMO). The climate normals include dry-bulb temperatures, dewpoint temperatures, wind speeds, pressure, and global horizontal solar radiation or sunshine hours. Many of the cities are missing a certain number of the climate variables. Another product that contains long-term normals for around 248 cities in the United States is the National Solar Radiation Data Base (NSRDB 1995), produced at the National Renewable Energy Laboratory (NREL). Publications describing this data set are available from NREL (Knapp et al. 1980). 3.12 Summary and conclusions A model and computer program have been developed for the purpose of generating synthetic weather data for input to building energy calculation software and sometimes as a replacement for real weather records when real data are hard to find (or are not available). The model has been shown to reliably simulate the variables of temperature, humidity, wind, and solar radiation—all important parameters in computing building beating and cooling loads. The model testing has been carried out on the basic weather statistics and has been found to be an acceptable representation of real data for the parameters normally regarded to be important to building thermal analyses.
Page 2:
Advanced Building Simulation
Page 5 and 6:
First published 2003 by Spon Press
Page 7 and 8:
vi Contents 8 Developments in inter
Page 9 and 10:
viii Figures 3.12 Relation between
Page 11 and 12:
x Figures 8.14 Workflow Modeling Wi
Page 13 and 14:
Contributors D. Michelle Addington,
Page 16 and 17:
Prologue Introduction and overview
Page 18 and 19:
Prologue 3 two topics that represen
Page 20 and 21:
Trends in building simulation 5 com
Page 22 and 23:
Reality Space averaged treatment of
Page 24 and 25:
Pervasive/invisible Web-enabled Des
Page 26 and 27:
Trends in building simulation 11 in
Page 28 and 29:
Trends in building simulation 13 ea
Page 30 and 31:
Trends in building simulation 15 ba
Page 32 and 33:
Trends in building simulation 17 1.
Page 34 and 35:
Trends in building simulation 19 th
Page 36 and 37:
and predictive simulation-based con
Page 38 and 39:
Trends in building simulation 23 Fu
Page 40 and 41:
Chapter 2 Uncertainty in building s
Page 42 and 43:
Uncertainty in building simulation
Page 44 and 45:
This completes the outline of the c
Page 46 and 47:
and inputs may be a formidable task
Page 48 and 49: 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
Page 96 and 97: Temperature (°C) or wind speed (m/
Page 100 and 101: References Simulation and uncertain
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
Page 106 and 107: a different physical state variable
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
Page 114 and 115: when employing Newton-Raphson solut
Page 116 and 117: Integrated building airflow simulat
Page 118 and 119: 25.9 15.9 13.7 10.2 7.2 4.2 0.0 10.
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
Page 146 and 147: y the room conditions. In fact, the
Page 148 and 149:
CFD tools for indoor environmental
Page 150 and 151:
Y/H Y/H 1 0.8 0.6 0.4 0.2 Plot-1 Pl
Page 152 and 153:
magnitude computing time than the R
Page 154 and 155:
CFD tools for indoor environmental
Page 156 and 157:
Chapter 6 New perspectives on Compu
Page 158 and 159:
New perspectives on CFD simulation
Page 160 and 161:
Page 162 and 163:
Page 164 and 165:
Page 166 and 167:
Page 168 and 169:
law must be invariant to a transfor
Page 170 and 171:
Page 172 and 173:
References New perspectives on CFD
Page 174 and 175:
Chapter 7 Self-organizing models fo
Page 176 and 177:
Self-organizing models for sentient
Page 178 and 179:
SOM topo- SOM site 1 graphy 1 1 1 1
Page 180 and 181:
Page 182 and 183:
Page 184 and 185:
DC EL1 DC EL2 MC EL_1 DC EL3 E 1 MC
Page 186 and 187:
technologies (Wouters 1998; Mahdavi
Page 188 and 189:
Page 190 and 191:
and photometric properties, as well
Page 192 and 193:
Page 194 and 195:
exploratory implementations. The fi
Page 196 and 197:
Page 198 and 199:
Page 200 and 201:
Page 202 and 203:
the electric light component of ill
Page 204 and 205:
Chapter 8 Developments in interoper
Page 206 and 207:
This chapter introduces the technol
Page 208 and 209:
Developments in interoperability 19
Page 210 and 211:
Page 212 and 213:
Instances subset A Building Model S
Page 214 and 215:
Import DT data Export DT data DT sc
Page 216 and 217:
Data-centric approach no explicit p
Page 218 and 219:
Page 220 and 221:
Page 222 and 223:
Glazing system? Window area? Monday
Page 224 and 225:
Page 226 and 227:
Page 228 and 229:
Page 230 and 231:
Page 232 and 233:
Chapter 9 Immersive building simula
Page 234 and 235:
Immersive building simulation 219 T
Page 236 and 237:
Immersive building simulation 221 E
Page 238 and 239:
Data generation Data preparation Ma
Page 240 and 241:
Shear Velocity Acceleration Curvatu
Page 242 and 243:
Immersive building simulation 227 F
Page 244 and 245:
etter than the others, they suffer
Page 246 and 247:
quality CRT screens, wide field of
Page 248 and 249:
Graphical representation Matrix (4D
Page 250 and 251:
Figure 9.22 Test room—section. Wa
Page 252 and 253:
(a) (b) Immersive building simulati
Page 254 and 255:
Immersive building simulation 239 m
Page 256 and 257:
Calibrated tracker data Figure 9.30
Page 258 and 259:
Immersive building simulation 243 g
Page 260 and 261:
Immersive building simulation 245 H
Page 262 and 263:
Epilogue Godfried Augenbroe and Ali
Page 264 and 265:
Index accountability 13 accreditati
Page 266 and 267:
performance assessment methods 109-
show all

Advanced Building Simulation

Create successful ePaper yourself

Delete template?

Save as template?