An integrated model-based approach to building systems operation

Proceedings of Clima 2007 WellBeing Indoors

An integrated model-based approach to building systems operation Ardeshir Mahdavi, Georg Suter, Angelika Susanne Metzger, Sergio Leal, Bojana Spasojevic, Szucheng Chien, Joseph Lechleitner, and Sokol Dervishi Vienna University of Technology, Austria Corresponding email: [email protected]

SUMMARY This presents a research effort toward an integrated model-based approach to energy-efficient operation of building systems. The ingredients of the corresponding concept are as follows: i) A comprehensive building state model underlines all operative entities and activities in the life-cycle of the building. This model includes information on building context (e.g. weather conditions), building topology, components, and systems, as well as building occupancy (user presence and actions); ii) The model is updated real-time via a sensory infrastructure including sensors for outdoor and indoor environmental conditions, occupancy presence and actions, state changes in control devices; iii) The building state model is provided to multiple applications pertaining to facility management and control systems. Such applications use various tools (including building performance simulation, trend analysis and learning algorithms) in order to anticipate the state of building and indoor climate as a result of alternative control options.

INTRODUCTION Complex buildings are frequently affected by shortcomings in the configuration and operation of building service systems, resulting in sub-par indoor climate conditions and poor energy performance. To address these concerns, this contribution presents a research effort toward an integrated model-based approach to energy-efficient operation of building systems. The ingredients of the corresponding concept are as follows: i) A comprehensive building state model underlines all operative activities in the life-cycle of the building. This model includes information on building context (e.g. weather conditions), building topology, components, and systems, as well as building occupancy (user presence and actions); ii) The model is updated real-time via a sensory infrastructure including sensors for outdoor and indoor environmental conditions, occupancy presence and actions, state changes in control devices; iii) The building state model is provided to multiple applications pertaining to facility management and control systems. Such applications use various tools (including building performance simulation, trend analysis and learning algorithms) in order to anticipate the state of building and indoor climate as a result of alternative control options. To demonstrate the viability of the proposed approach, a 1:1 two-cell office space has been set up in our laboratory. The facility is equipped with systems for heating, ventilation, lighting, and shading. The state of weather conditions is monitored via a weather station. Indoor environment data are collected via sensory units measuring temperature, relative humidity, air flow speed, illuminance, etc. Changes in the configuration of the cells and workstations (e.g. location of furniture and partition elements) as well as occupancy information are captured via an optical location-sensing system. These data are updated regularly and provided to the building systems control unit along with users' feed-back regarding their indoor climate


preferences. The building systems control unit uses building performance simulation to predict the implications of alternative control device states and identifies (through comparison and ranking of the simulation results) and brings about a configuration of the control device states that is preferable both in terms of energy efficiency and indoor climate. We first describe two instances of automated model actualization, namely the actualization of a context mode (sky luminance distribution pattern) that can be used for real-time simulationbased lighting systems control, and the actualization of the room configuration model via location sensing. We then describe the principles and application of a model-based systems control strategy using the example of lighting controls. Subsequently, we illustrate the development of a test-bed for the integrated realization of a model-based multi-system energy-efficient building systems control strategy. APPROACH AND RESULTS To realize a model-based building control strategy, the building must possess a model of itself, including both context and components. To illustrate this point in this section, we first use the case of dynamic generation of sky luminance model that can be used for real-time simulations toward lighting systems control. We then illustrate the process of room geometry and configuration model generation and updating via location sensing. Sky-Scanning The sky model is generated on a real-time basis using calibrated digital photography. Toward this end, the building’s weather station is augmented with a digital camera with a fish-eye converter. From images, the sky luminance model is extracted in terms of distinct luminance values for 256 sky patches (Figure 1). Digital images of the sky are continuously taken, analyzed and calibrated real-time to construct the sky model for the simulation application. The calibration is necessary, as the camera is not a photometric device. It is possible, however, to derive reliable photometric data from properly calibrated digital images [1]. The approach to calibration involves measuring global horizontal illuminance data simultaneously with digital sky photography.

Figure 1. Weather station and the pattern for sky luminance mapping. The external illuminance data is obtained from the building's weather station. For each image, the initial estimate of the illuminance resulting from all sky patches on a horizontal surface can be compared to the measured illuminance. The digitally-derived sky patch luminances can be corrected to account for the difference between measured and digitally estimated horizontal illuminance levels [2].


Location Sensing In previous studies, we showed how a simulation-based lighting systems control [3] [4] may be supported by provision of dynamically updated room models that are generated based on the vision-based location-sensing system VIOLAS [5]. Thereby, it was shown how a set of networked pan-tilt cameras in a facility can regularly provide updated models of room geometry and objects within the buildings that can be used for facility management and building control applications. VIOLAS involves five major blocks, whereby each block has a distinct role in context data extraction (Figure 2).

Figure 2. "Sensing core" in VIOLAS. The Hardware Interface block permits multi sensor data acquisition. In our first attempt, due to the possibilities introduced by the usage of integrated computing, netcams equipped with pan/tilt units were used. However, netcams also generate relatively low-quality data due to data compression resulting in low resolution and blurred images without sharp details. Moreover, pan-tilt netcam solutions are rather bulky, expensive, and involve moving elements. Due to these circumstances, we proposed to study the possibility of improving the VIOLAS system by means of digital cameras with fisheye lenses. Already employed within VIOLAS, the location sensing module is adapted from TRIP system (cp. [6] and Figure 3). A circle on the target plane generates an ellipse on the image plane of the camera. From the known parameters of the ellipse, it can be back-projected to the original circle enabling the extraction of the orientation and the position of the target plane with respect to the camera origin [7]. In order to implement this algorithm, barcode-like tags with circular marks are used. Any regular black-and-white printers can be used to reproduce these tags enabling lowcost and low-maintenance tags without the necessity of power input. Identification is achieved by the codes printed around the circular mark (reference circle). Contrasting with the TRIP system that uses ternary coding, VIOLAS uses binary coding, where the tags are divided into


16 equal sectors (Figure 4) resembling pie slices. The presence or absence of the black mark on the sector denotes the 1 or 0 coding respectively. The pattern of "0111" code sequence defines the start bit sequence, and is not repeated in the code string to avoid ambiguity.

Figure 3. "Pose from circle" algorithm. (X,Y,Z) denotes the coordinate system of the image plane whereas (X’,Y’,Z’) denotes the coordinate system of the target plane. The outcomes of the algorithm are the parameters of the transformation between two coordinate systems.

Figure 4. Tag structure is illustrated with a sample tag coded with 0111011010101101 data string (even-parity = 1). Identification number corresponds to 1709 in decimals.

With the remaining 12 sectors we are able to encode 2031 distinct tagged objects. The TRIP system divides the location sensing procedure into two phases. In the first phase, "target recognition", where the tags are detected, parameters of the reference ellipse (projection of the reference circle on the camera image) are extracted and the identification numbers are decoded. In the second phase, "pose extraction", the locations of the tags are computed from the outputs of the first phase [6]. VIOLAS improves the original TRIP method by incorporating two additional algorithms (Figure 5). An "adaptive sharpening algorithm" [8] on the input image prior to "target recognition" (Figure 5) is applied, resulting in an enhanced output image. As the pixel resolution of the tag images decreases with increasing distance between the tags and the camera, code identification becomes difficult, despite the fact that the tags are being detected and reference ellipses are being correctly extracted. In order to resolve this problem, "edge-adaptive zooming" [9] is applied locally to spurious tags from which the code could not be deciphered or validated. Edge-adaptive zooming, rather than its equivalents such as bilinear and cubic interpolation, enhances the discontinuities and sharp luminance variations in the tag images. This procedure is recurring until the "target recognition" is successful or the zoomed image region loses its details (Figure 6). In the latter case, a false alarm or an unidentified tag will be retrieved.


Figure 5. Algorithm flow of object identification and location sensing in the "sensing core".

Figure 6. Example of a successful tag localization.

Model-Based Control To demonstrate a model-based building systems control strategy, we consider the case of lighting and shading systems control in an office space (see Figure 7) [10]. This office's two windows are equipped with automatically controllable blinds. Artificial illumination is provided by two free-standing luminaires. The room model entails information about room geometry, furniture, the location and size of windows as well as the physical properties of room components (such as reflectance and transmittance) as well as the position of virtual sensors that monitor pertinent performance parameters (such as illuminance levels or glare indices). The room is equipped with the aforementioned location-sensing system, which automatically tracks changes in the position of moveable furniture elements (including the aforementioned luminaires). Presence of the people in the room (at the workstations) is monitored with occupancy sensors. User preferences (e.g. desirable illuminance levels, relative weights for objective function) can be communicated to the lighting control application via occupants' and facility manager's computers. The room model provides the basis for the system's internal representation and is updated dynamically using an opticallybased location-sensing system.

Figure 7. Illustration of the test space (L1, L2: Luminaires; B: Blind; E1 and E2: virtual illuminance sensors).


Control Devices and Control State Space Three control devices are considered, namely the two luminaires (L1, L2) and the window blinds (B). In the control scenarios considered in this paper, the two blinds are controlled simultaneously. As a primary indicator of lighting performance, we considered the two mean illuminance levels Em1 and Em2. Since all these devices affect both Em1 and Em2, a central control instance (C) is required to coordinate the three devices toward the most preferable control state. The overall behaviour of the control system is determined through a utility function (UF). The overall objective of the control process is to maximize UF, which, in our test runs, pertains to preferences for illuminance levels, cooling load, and electrical energy consumption. The following scenario involving a control cycle is repeated regularly (in this case, every 15 minutes). At every time step ti, each device (i.e., L1, L2, B) submits to the controller application C a list of candidate device states for time step ti+1. In the present case, each device submits four alternative options. These options are: the device's current position, the 2 adjacent positions, and a fourth – randomly chosen – option from the rest of the device's control state space. Thus, the control application considers the resulting overall option space involving a maximum of 64 distinctive control states. To illustrate the working of the abovedescribed control method, we documented the operation of the system in the course of fifteen days (fourteen days in May and one day in June 2005). In this case the control systems reassessment of the desirable control state occurs regularly every 15 minutes. The following figures illustrate this operation in terms of system recommendations and its performance. Given this paper’s space limitation, figures 10 to 12 exemplify data only for one day, namely 14th May 2005, in terms of: measured external global horizontal illuminance and control system’s predictions of the illuminance levels Em1 and Em2 as a result of the control system’s recommended shading and luminaire states (Figure 8); recommendations of the control system for dimming positions of the two luminaires and the blind position (Figure 9). 120000

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Figure 9. System's recommendations for the luminaires' dimming states and the blind positions.


Development of the Test Bed The test bed is being started "from scratch" as a full-scale mock-up of two office rooms in a test booth located in the building physics laboratory at TU Wien. The current objectives of the test bed are to enable replication of the results of the above mentioned lighting control study and extension of the lighting control model to include a system for heating, ventilation and air-conditioning (HVAC). Current plans for test bed equipment installation in the existing light-weight structure include: i) HVAC system; ii) Electrical lighting system; iii) Active daylighting system; iv) Enclosure systems; v) Furniture systems; vi) Control systems; and vii) Information systems. An overview of the test bed layout is shown in Figure 10, below. Task Light

Ambient Light Motorized Window & Interior Blind

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Figure 10. Interior and plan views of the test bed under development. Simulation-assisted control involves two types of information, those typically transmitted over local area networks, such as data from local servers and weather stations, and data exchanged between the controller and the actual test bed, e.g. for sensor – actuator interaction. Most commercially available building systems for automation communicate on a LON-bus [11]. The test bed uses both bus systems for an authentic demonstration of the model-based control strategy. The test bed can be seen as a continuation of previous experiments in simulation-assisted control. Table 1 shows an overview of control parameters and illustrative control states.

Table 1: Illustrative represententation of the control state space of the test bed. Control Parameter Natural Ventilation

Control Variable

Actuator

Window Tilt Position

Window Actuator (% open)

States (Values) 1. 0% 2. 100% 1. 2. 3. 4. 5. 6. 7.

vertical & fully down horizontal & fully down vertical & 2/3 down horizontal & 2/3 down vertical & 1/3 down horizontal & 1/3 down fully pulled up

Daylighting

Blind / Louver Position (vertical & horizontal)

Blind Actuator (shading rate, descending)

Electrical Lighting

Dimming Level of Ceiling Fixtures (power output in Watts)

Lighting Actuator (% dimmed, descending)

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Supply Air Temperature (dry-bulb temperature, °C)

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CONCLUSIONS We described a test bed for the prototypical realization of an integrated (multi-system) modelbased building control strategy. A comprehensive self-actualizing building model is at the core of the implementation. It encompasses information regarding building context, components and processes. Regularly updated, it provides a real-time source of information for facility management and building systems control operations. Ongoing research is expected to expand the already developed simulation-based lighting and shading control systems to cover further environmental systems for heating, cooling and ventilation. Moreover, innovative user-interface systems are being tested to facilitate an intuitive interaction modus between occupants and the building's environmental control systems. ACKNOWLEDGEMENT The research presented in this paper was supported, in part, by grants from the Austrian Science Foundation (FWF), project number L219-N07. REFERENCES 1.

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