Advanced Building Simulation

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Self-organizing models for sentient buildings 181 time-step. Detailed lighting simulation runs are computationally intensive and require considerable time. Obviously performing this number of simulations within a time-step (of, say, 15 min) is not possible. To reduce the size of the segment of the control state space to be explored, one could either couple devices (e.g. by dimming the four space luminaires in terms of two coupled pairs) or reduce the number of permissible device positions. An example for the latter would be to consider, at each time-step, only three dimming states for each luminaire, namely the status quo, the immediate higher state, and the immediate lower state. In the present case, this would mean that D � 2 and P � 3, resulting in 9 electrical lighting options. Considering 4 candidate louver positions, the total number of required simulations would be reduced to the manageable number of 36. The concurrent simulation-based assessment of daylight and electrical light options allows for the real-time incorporation of changes in room and aperture configuration, as well as flexibility in the definition of the relevant parameter for performance variables (such as the position of observer, etc.). However, the limitation of possible dimming options at each time-step to the immediate adjacent positions may result in the inability of the search process to transcend local minima and/or maxima. This problem can be handled to a certain degree by considering additional randomly selected control state options to be simulated and evaluated in addition to the default “greedy” search option in the control state space (cp. Section 7.4.5.2). The second approach to the generation and evaluation of alternative control options involves a sequential procedure. In this case, first, the preferable louver position is derived based on the methodology described earlier. The result is then combined with a preprocessed matrix of various luminaire power levels. This matrix (or look-up table) can be computed ahead of the real-time control operation based on the assumption that the incident electrically generated light at any point in the space may be calculated by the addition of individual contributions of each luminaire. The matrix needs only to be regenerated if there is a change either in the configuration of interior space or in the number, type, or position of the luminaires. The advantage of this approach is the possibility to reduce computational load and extend the search area in the control state space. The typical time interval between two actuation events (e.g. change of louver position and/or change of the dimming level of a luminaire) would then be generally sufficient to allow for the simulation of an increased number of louver positions. Combining the results of the selected louver settings with the matrix of electrical lighting states does not require real-time simulation and is thus efficient computationally. As a result, a larger number of dimming options may be considered and evaluated toward the selection of the preferable combined daylighting and electrical lighting settings. The following steps illustrate this process for a generic time-step as experimentally implemented in IW: 1 Outdoor light conditions, the current louver position, luminaire power levels, and the current time were identified (Table 7.5). 2 <strong>Simulation</strong>s were performed for each of the eight candidate louver positions based on the input data. Calculated performance indices for each louver position were further processed to generate the utility value (UF) based on the preference indices and corresponding weights (Table 7.6). Subsequently, the louver position that maximizes utility was selected (105�).
182 Mahdavi Table 7.5 Initial state as inputs to simulation Year Month Day Hour I global I diffuse E global � n (lvr) L1 L2 L3 L4 (W/m 2 ) (W/m 2 ) (lx) (degree) (%) (%) (%) (%) 1998 5 12 15 343 277 39,582 30 50 40 40 50 Note L1, L2, etc. are current luminaire input power levels Table 7.6 Performance indices and the utility values for each optional louver position � n�1 (lvr) E m U E DGI CGI GCRT Q P UF (degree) (lx) (W) (W) 0 291 0.849 4.31 0 0.744 4.29 0 0.623 15 249 0.856 4.14 0 0.752 3.84 0 0.593 30 251 0.855 4.28 0 0.749 3.59 0 0.594 45 263 0.870 4.39 0 0.742 3.54 0 0.606 60 280 0.859 5.56 0 0.739 3.46 0 0.617 75 310 0.430 5.81 0 0.731 3.57 0 0.665 90 331 0.840 5.98 0 0.707 3.90 0 0.665 105 337 0.841 6.00 0 0.747 4.47 0 0.670 Note Weights: w Em � 0.45, w U � 0.2, w DGI � 0.05, w CGI � 0.03, w GCRT � 0.1, w Q � 0.12, and w P � 0.05. 3 Another round of simulations for the selected louver position was performed to generate intermediate data for the calculation of glare indices when the selected louver position is combined with various sets of luminaire power level configurations. Calculated glare component parameters (daylight component) include background luminance, luminance of each window patch for DGI calculation, direct and indirect illuminance on the vertical surface of the eye for CGI calculation, as well as the luminance on the computer screen for GCRT calculation. 4 For each luminaire, five steps of candidate power levels (current power level plus two steps below and two steps above) were identified. Then, from the pregenerated look-up table, all 625 (5 4 ) power level combinations were scanned to identify the corresponding illuminance distribution and power consumption along with the glare component parameters (electrical light component) for CGI and GCRT calculations. 5 Final values of glare indices were generated by combining the glare component parameters (both daylight component and electrical light component) calculated in step 3 and 4 for each louver–luminaire set. This is possible since the pre-calculated glare component parameters are additive in generating the final glare indices. 6 The louver position and luminaire power levels for the preferable control state were identified by selecting the one option out of all 625 sets of louver–luminaire control options that maximizes the utility value (cp. Table 7.7). 7 Analog control signals were sent to the louver controller and luminaire ballasts to update the control state.
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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
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Table 2.1 Categories of uncertain m
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∆C p (-) 1.6 1.2 0.8 0.4 0.0 -0.4
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S d (h) 80 60 40 20 0 -80 2 Uncerta
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2.4.2 Uncertainty in wind pressure
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an experiment involving structured
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with their uncertainties, in terms
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Table 2.5 Expected utilities for de
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y a performance gain on another asp
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Simulation and uncertainty: weather
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makes every month normalized to a z
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Dry-bulb temperature (°F) 60 50 40
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(b) Compute today’s average tempe
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ackward. First, the average daily h
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a��sin(�)* ln[I SC * � D] (
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H = Total daily horizontal insolati
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Temperature (°C) or wind speed (m/
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Horizontal insolation (MJ/m 2 /day)
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References Simulation and uncertain
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Chapter 4 Integrated building airfl
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(a) (b) zone 6 5 4 3 2 1 3 1 Pre-he
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a different physical state variable
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West Zone n Zone m Figure 4.5 Examp
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Node n Figure 4.6 An example two zo
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The nodal pressures are then iterat
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when employing Newton-Raphson solut
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Integrated building airflow simulat
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25.9 15.9 13.7 10.2 7.2 4.2 0.0 10.
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Air temperature (°C) 40.0 35.0 30.
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It was found that the differences a
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of the problem. In any event, calib
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Resolution CFD Decision Reduced Dec
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Although most of the basic physical
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Chapter 5 The use of Computational
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CFD tools for indoor environmental
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the Reynolds average rules can be s
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3m Control room Enclosure 5.5 m Out
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Page 172 and 173: References New perspectives on CFD
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Page 178 and 179: SOM topo- SOM site 1 graphy 1 1 1 1
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Page 212 and 213: Instances subset A Building Model S
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Page 238 and 239: Data generation Data preparation Ma
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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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