Sensor-driven models for improved building services

This proposal is motivated by the need to improve the quality and cost effectiveness of services in the building industry. In Austria, the building industry accounts for approximately 12% of GDP. The improvement potential has been estimated to be as high as 30% of overall spending in building design, construction, and operation. We believe that recent advances in low cost wireless communication and sensor technology can bring about significant improvements in building services. Products that take advantage of sensor technologies exist, for instance, for the servicing of elevators. However, in our view these products capture only a small fraction of the information that could be useful for builders, contractors, manufacturers, operators, occupants, designers and building owners. What is lacking is a systematic and comprehensive approach to collecting facility state information throughout the building life cycle and making it accessible to interested parties and applications. Towards this end, we propose to develop a framework for sensor-driven, high-resolution building models which can automatically reconfigure themselves in response to certain types of events occurring in a facility over time. We will investigate several key conceptual and technical issues, which include model resolution and derivation, sensing system configuration, event handling, and system performance. To demonstrate the feasibility of the proposed framework, we will design and implement a prototype lighting services system, and test it within a full-scale testbed of an office space. The space will be tagged with a combination of illuminance and motion sensors as well as radio-frequency identification transponders. Sensor readings are used to automatically construct a digital model reflecting the physical space configuration. A simulation-assisted lighting control mechanism can query that model and initiate appropriate action to optimize lighting conditions in the space. The proposed research aims to demonstrate the following benefits of sensor-driven high resolution models for building service operations: automatic rather than manual maintenance of as-built building models; greater space time accuracy and resolution of such models; realtime snapshots of a facility`s state; and ability to track modifications over time.

The goal of this project was to implement, test, and document a systematic approach to the conception of truly intelligent (sentient) buildings. Such buildings, we hypothesized, must: 1) possess an internal digital representation of their context, components, and processes; 2) must be able to automatically and autonomously update this representation over time; 3) use this representation to support facility management and system operation processes. Towards this end, we investigated the feasibility of sensor-driven, high-resolution building representations which can automatically reconfigure themselves in response to certain types of events occurring in a facility over time. Several key conceptual and technical issues were explored, implemented and partially tested in prototype systems, including model resolution and derivation, sensing system configuration, event handling, and system performance. The lighting services domain was used as a test case. A simulation-assisted lighting control mechanism can query a sensor-driven building;model and initiate appropriate action`to optimize lighting conditions in the space. The research project demonstrated in principle the following benefits of sensor-driven high resolution models for building service operations: automatic rather than manual maintenance of as-built building models; greater space time accuracyand resolution of such models; realtime snapshots of a facility`sstate; and ability to track modifications over time. We demonstrated how: 1) the building`s context (sky) model can be automatically updated using digital sky luminance mapping; 2) how changes in the internal configuration of a`buliding`s spaces can be dynamicallycaptured using a vision- based object and occupancy sensing system; and 3) how the resulting representations can be used to support a simulation-assisted approach to lighting systems controls in buildings.