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the electric light component of illuminance drops (electric light state decreases) as<br />

daylight increases. The total interior illuminance is generally maintained within the<br />

set-point range (400–600 lx).<br />

7.5 Conclusion<br />

This chapter described the concepts of sentient buildings and self-organizing building<br />

models. It explored the possibility of integrating primary models of buildings’ composition<br />

and behavior in higher-level building control systems. The realization of such a<br />

meta-mapping functionality in the building operation system architecture could extend<br />

the role and applicability of dynamic (behavioral) building representations beyond<br />

their current use in computational performance-based building design support.<br />

Computational performance simulation codes and applications could become an integral<br />

part of the methodological repertoire of advanced building operation and controls<br />

systems. Thus, a larger set of indoor environmental performance indicators could be<br />

considered toward indoor climate control. Beyond mere reactive operations based on<br />

environmental sensing, simulation-based building control allows for proactive evaluation<br />

of a richer set of control options, and allows for the reduction of the sensors<br />

needed for the real-time environmental performance assessment. A particularly attractive<br />

feature of the proposed model-based strategy lies in its potential for a transparent<br />

and high-level integration of multiple control agenda. Thus, complex control strategies<br />

may be formulated to simultaneously address habitability, sustainability, and feasibility<br />

considerations in providing appropriate levels of building performance.<br />

References<br />

Self-organizing models for sentient buildings 187<br />

Broz, V., Carmichael, O., Thayer, S., Osborn, J., and Hebert, M. (1999). “ARTISAN: An<br />

Integrated Scene Mapping and Object Recognition System.” American Nuclear Society In<br />

8th Intl. Topical Meeting on Robotics and Remote Systems, American Nuclear Society.<br />

Chang, S. and Mahdavi, A. (2002). “A hybrid system for daylight responsive lighting control.”<br />

Journal of the Illuminating Engineering Society, Vol. 31, No. 1, pp. 147–157.<br />

De Ipina, D.L., Mendonca, P., and Hopper, A. (2002). “TRIP: a low-cost vision-based location<br />

system for ubiquitous computing.” Personal and Ubiquitous Computing Journal, Springer,<br />

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Eggert, D., Fitzgibbon, A., and Fisher, R. (1998). “Simultaneous registration of multiple range<br />

views for use in reverse engineering of CAD models.” Computer Vision and Image<br />

Understanding, Vol. 69, pp. 253–272.<br />

Einhorn, H.D. (1979). “Discomfort glare: a formula to bridge difference.” Lighting Research &<br />

Technology, Vol. 11, No. 2, pp. 90.<br />

Faugeras, O., Robert, L., Laveau, S., Csurka, G., Zeller, C., Gauclin, C., and Zoghlami, I.<br />

(1998). “3-D reconstruction of urban scenes from image sequences.” Computer Vision and<br />

Image Understanding, Vol. 69, pp. 292–309.<br />

Finkenzeller, K. (2002). RFID-Handbuch. Hanser. ISBN 3-446-22071-2.<br />

Hopkinson, R. G. (1971). “Glare from window.” Construction Research and Development<br />

Journal, Vol. 2, No. 4, pp. 169–175; Vol. 3, No. 1, pp. 23–28.<br />

Mahdavi, A. (2003). “Computational building models: theme and four variations (keynote).”<br />

In Proceedings of the Eight International IBPSA Conference. Eindhoven, The Netherlands.<br />

Vol. 1, ISBN 90-386-1566-3, pp. 3–17.<br />

Mahdavi, A. (2001a). “<strong>Simulation</strong>-based control of building systems operation.” <strong>Building</strong> and<br />

Environment, Vol. 36, Issue No. 6, ISSN 0360-1323, pp. 789–796.

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