Design and Instrumentation of an Intelligent Building Testbed

Matt Minakais1, Sandipan Mishra2, John T. Wen3, Luc Lagace2, Timothy Castiglia1

Abstract—This paper presents the design and instrumenta-tion of a 3:5-scale physical testbed of an intelligent building.The testbed is designed to enable performance evaluation ofvarious temperature control algorithms in a controlled andrepeatable setting. Key features of this testbed include fullycontrolled mass flow and supply air temperature, a sensor-rich environment (producing both temperature and energymeasurements), control of the ambient temperature aroundthe testbed, and a modular structure with multiple zones andvarying degrees of thermal and mass flow coupling. The testbedis partitioned into 6 rooms; the interconnected structure ofthese rooms allows us to study the thermal coupling that occursbetween adjacent zones and to explore the challenges associatedwith under-actuated zones. Air conditioning is provided bywall mounted thermoelectric coolers controlled wirelessly froma central computer. A unique feature of this testbed is itsplacement inside a larger temperature-controlled enclosure,which allows simulation of time-varying ambient weather.Precise control of ambient temperature provides a means forrobust comparison and evaluation of control architectures. Asa preliminary demonstration, we present experimental resultscomparing the performance of a decentralized proportional-integral controller and a decentralized adaptive controller undertime-varying ambient temperature.

I. INTRODUCTION

There has been increasing emphasis on energy conser-vation in buildings, specifically on the control of heating,ventilation, and air conditioning (HVAC) systems. Thesesystems have been targeted due to their large energy footprintand the potential value of adding advanced control strategies.Numerous schemes have been proposed that utilize moresophisticated control algorithms compared to traditional pro-portional and on/off control used in most buildings today.These algorithms include model predictive control (MPC)[1]–[4], feedforward control [5], [6] and neural networks [7]–[10]. In the absence of hardware validation, high fidelitysimulation has been used to assess the effectiveness ofthese methods [2], [11], [12]. These simulations are mostlyperformed using physics-based simulation programs suchas EnergyPlus [13] or, less frequently, computational fluiddynamics (CFD). While these simulations may deliver highfidelity, they rely heavily on specific knowledge of buildingconstruction materials and architecture, and can fail to cap-ture the full dynamics of the system when these factors arenot considered carefully. Demonstration on actual buildingstypically involves outfitting an existing building with sensorsand gaining control of the HVAC system [1], [4]. However,it can be difficult to make accurate comparisons since distur-bances (ambient weather, sunlight, temperature, occupancy,etc.) can change between trials, causing experiments to beperformed under non-repeatable conditions.

To provide a better framework for standardization andvalidation of algorithms, some researchers [3], [14] havebuilt physical outdoor testbeds. While these testbeds offer fullHVAC control and high fidelity, they are subjected to normalenvironmental disturbances, again difficult for repeated trialsunder identical conditions. Thus, there is a need for a wellinstrumented testbed that can mimic typical building behaviorunder repeatable conditions for the evaluation of modelingand control strategies

This paper describes the design and instrumentation ofa 3:5-scale physical model of a college dormitory, placedinside a temperature-controlled enclosure. The testbed issensor rich, allows control of both mass flow and supplyair temperature as well as ambient (outside) temperature,and is designed to simulate a space with interconnectedzones that exhibit mass flow and thermal coupling. As apreliminary demonstration of these capabilities, we comparethe performance of two feedback controllers: decentralizedproportional-integral (PI) control and PI control with ambienttemperature adaptation.

The paper is organized as follows: Section II outlinesthe requirements desired for a building testbed. SectionIII describes the physical layout and construction of thespace. Sections IV and V discuss the hardware and softwarearchitectures, respectively. In Section VI, we obtain a math-ematical model of the system and experimentally validatethe model. Section VII presents a side-by-side comparisonof two feedback controllers, followed by concluding remarksin Section VIII.

II. TESTBED REQUIREMENTS

This section outlines the necessary features and desirableattributes for a temperature controlled building testbed.

Sensor-rich Environment - Temperature measurementwith fine granularity is critical for the testbed, thereforethermocouples should be placed densely throughout all roomsas well as the ambient enclosure to capture thermal floweffects that are typically unobservable in a live to-scalebuilding. Real-time energy measurement is also a key de-sirable capability. Finally, we wish to monitor both supplyair temperature and mass flowrate at each input location.

Full Control and Repeatability - To properly implementa control strategy, we need to possess full control of thetestbed’s HVAC system, including the ability to indepen-dently control flowrate and supply air temperature. Addition-ally, we require the ability to produce and repeat ambienttemperature trajectories in the outer enclosure. This enablesthe testbed to mimic the typical thermal disturbances and

2015 IEEE International Conference onAutomation Science and Engineering (CASE)Aug 24-28, 2015. Gothenburg, Sweden

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cycles that a building experiences, and to do so in a repeatablefashion suitable for comparison between trials.

Similarity to Full-scale Buildings - The scaled testbedshould reflect the actual behavior of buildings, notably theinterconnection of multiple thermal zones. Doors betweenadjacent zones are necessary to allow variable control of themass flow exchange between zones. Similarly, windows onambient-adjacent zones will allow for direct air transfer withthe ambient environment, as well as the capability to produceradiant heat input.

Wireless Connectivity, Remote Access and Modularityof Software-Hardware Architecture - The ability to runexperiments remotely is a desirable attribute for any testbed.To address this, there should exist a protocol for gainingremote access to the central computer. Furthermore, thecentral computer should be un-tethered from the physicaltestbed, facilitated by wireless communication between thecentral computer and all sensors and actuators. This tends toalign with existing state-of-the-art sensor-actuator networkswhich are fully wireless. The key importance of this attributeis modularity; i.e., the ability to seamlessly add, remove, andrelocate sensors.

In the following sections, we describe the physical con-struction and layout, hardware instrumentation, and softwarearchitecture of the testbed.

III. PHYSICAL LAYOUT

In this section, we discuss the physical architecture andconstruction of the testbed. The outer enclosure measures7.3m×10.7m×2.5m. The dimensions of the inner buildingare 5.2m × 7.6m × 2m. On one side of the space there arethree small rooms, separated from two larger rooms by acentral hallway. See Figure 1 for a detailed floor plan ofthe space, including the locations of temperature sensors anddimensions of all rooms. In this figure, double-circles denotea pair of sensors (floor and ceiling) and single circles denotea single sensor placed midway between the floor and ceiling.There are additional sensors located at each cooling deviceto measure supply air temperature, which are not shown.

All walls (interior and exterior) are 11.43cm thick, con-structed from 1.27cm-thick plywood sheets and 3.81cm ×8.89cm studs spaced 40.64cm apart. Standard R-13 fiberglass insulation fills the space in each wall. Floors and ceil-ings are also 11.43cm thick and have identical constructionto the walls. Doors are positioned in the center of each wallwhich connects a room to the central hallway, as denoted inFigure 1. Sliding doors are chosen to provide a more explicitdefinition of how open a door is. Each small room containsone window, and each large room contains two. Windows arepositioned 0.67m above the floor at the locations denoted inFigure 1. Each window has dimensions 0.56m× 0.81m andprovides a 0.56m× 0.30m opening when fully opened.

IV. HARDWARE INSTRUMENTATION

This section describes the hardware architecture of thetestbed in three subsections. We first present the sensing ar-chitecture and data acquisition hardware, followed by the ac-tuator instrumentation. Finally, we discuss the infrastructure

Fig. 1. Physical layout of testbed. All walls, floors, and ceilings are 11.43cmthick. Temperature sensor locations are shown as circular markers. Thelocation of the thermoelectric coolers are denoted by the symbol).

that ties these elements together with the central controller.Table I lists the primary hardware used in instrumentation,accompanied by brief descriptions and part numbers.

A. Sensing

Temperature information is collected through a total of80 J-type thermocouples installed throughout the space, 10thermocouples in each small room (8 to measure roomtemperature and 2 to measure supply air temperature), 15thermocouples installed in each large room (12 to measureroom temperature and 3 to measure supply air temperature),8 sensors installed in the hallway, and 12 sensors placedon the exterior of the building to measure the temperatureof the ambient enclosure. Figure 2 shows the placement ofthermocouples within each room.

(a) Small Room (b) Large Room

Fig. 2. Temperature sensor locations inside rooms.

This placement pattern is chosen to measure temperaturedistribution not only on the X-Y plane but also as a functionof height. Although this level of sensor density may not berealistic in practice, it allows us to study the impact of sensorplacement and make comparisons with CFD simulations.Figure 3 illustrates the need for dense sensor placement,as seen from the rise in temperature with height. For this

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TABLE IINSTRUMENTATION PARTS LIST

Part (Quantity) Manufacturer Part Number Specifications1. Thermoelectric Cooler (12) Hoffman TE162024020 200W, 24V2. Power Supply (12) Mean Well RSP-500-27 500W, 27V3. Current Sensor (12) Allegro MicroSystems ACS712 Hall Effect, 30A4. Thermocouple (80) National Instruments 745690-J002 J-Type, 2m5. NI myRIO-1900 (2) National Instruments 782693-01 40 DIO, 16 AI06. NI Wireless DAQ Chassis (5) National Instruments 781497-01 802.11 Wi-Fi7. NI Thermocouple Module (5) National Instruments 780493-01 16 Channel, 24 Bit8. York Affinity Furnace (1) York YP9C060B12MP12C 1200 CFM

experiment, one room is controlled (with proportional con-trol) to reach a 22.4◦C setpoint and allowed to settle. Adifference of over 1.8◦C is observed between sensors spacedonly 1.7 meters apart vertically. The clustering of the sensorreadings demonstrates the temperature gradient along thevertical direction.

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Fig. 3. Temperature in one room is regulated (using proportional control)to a 22.4◦C setpoint. The temperature measurements demonstrate thevariations in temperature with respect to the height of temperature sensors.

In addition to temperature distribution in the testbed, wewish to monitor energy consumption of the HVAC system, asthis will serve as a primary metric in controller comparison.To provide this capability, we utilize the ACS712 Hall-Effect-based current sensor, placed at the power supply of eachinput. This information, combined with a known voltage, isused to compute the system’s the total power consumption.

B. Actuation: Heating and Cooling System

To mimic the behavior of a building’s HVAC system, wewish to control the heat input in Watts, q, into each room.This is not directly controllable, rather it is a function of thetemperature of the air added to the room (supply air tem-perature, Ts) and the amount of supplied air (mass flowrate,ms), which have the units ◦C and kg/s, respectively. For aparticular room i, these terms are related by:

ms,i =qi

cp(Ts,i − yi)(1)

Here, cp is the specific heat capacity of dry air, and yi isthe temperature of room i. To obtain control of Ts and ms,

thermoelectric coolers (TEC) are installed along the exteriorwalls of the testbed. Each small room is instrumented with2 coolers, while the larger rooms contain 3 coolers each,as shown in Figure 1. The hallway is not directly actuated.Thermoelectric coolers utilize the Peltier effect (see [15] fora good description of TEC functionality) in combination withfans to produce chilled air, by directing room-temperature airacross a cold surface (note that a TEC can be used for heatinginstead of cooling by applying a negative voltage to thePeltier device). The mass flow rate is controlled by changingthe fan speed, while the supply air temperature is controlledby changing the voltage applied across the Peltier device.Note that control of both supply air temperature and massflowrate is not always obtainable in practice, however thesecan be arbitrarily constrained to match an existing system.Figure 4 shows a schematic of the TEC system. It is key tonotice that the fan draws in air from the room and cools it toTs before recirculating it back into the room. Ventilation isrouted to each TEC to prevent the exhaust air from interferingwith the temperature of the ambient enclosure.

Fig. 4. TEC cooling process. Supply air temperature (Ts) is controlledby varying the voltage on the Peltier device, which varies the temperaturedifferential between the hot and cold surfaces. Mass flowrate (ms) iscontrolled by varying fan voltage (i.e., the fan speed).

The temperature of the cold surface is a function ofthe voltage supplied to the Peltier device, allowing variablecontrol of the supply air temperature, Ts. Since fan speed isdependent on the voltage applied to the motor, we can obtainindependent and variable control of both desired control in-puts: Ts and ms. To facilitate this, a microcontroller providesa pulse width modulated (PWM) signal, which is amplifiedto drive the fan and Peltier device. In this manner, we cancontrol both input parameters by adjusting the duty cycles ofthe two PWM signals. To determine the relationship between

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duty cycle and control inputs, a calibration experiment isperformed for the full range of possible duty cycles and thecorresponding control input. We use the inverse of this mapto determine the appropriate duty cycle for a desired input.

The ambient temperature, T∞, is controlled by a separateheating system powered by a furnace in the outer enclosure.Vents are positioned around the testbed to ensure uniformheating of the ambient environment. To control the furnacewirelessly, the manual thermostat is modified to receivea signal from a microcontroller, providing wireless on/offcontrol of the furnace.

To demonstrate the ability to produce repeatable ambienttemperatures, we command the ambient temperature to fol-low a sinusoidal reference signal with amplitude 2.78◦C, pe-riod 12 hours, and a 28.9◦C offset. The furnace is controlledvia on/off control with hysteresis. When the furnace is turnedon or off, a mechanical delay exists which causes undershootor overshoot respectively. To resolve this, an acceptable errorbound is chosen, and then a set of tighter hysteresis boundsare chosen heuristically to satisfy the given constraints. Forthis experiment, we allow temperature error of ±0.5◦C,which corresponds to hysteresis bounds of ±0.1◦C. Theresults of this experiment are shown in Figure 5.

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Fig. 5. Tracking an ambient temperature reference using on/off control withhysteresis. Acceptable error bound is set at ±0.5◦C.

C. Hardware Instrumentation Infrastructure

This section describes the architecture which connects thesensor network, actuation system (both the TECs and theambient furnace), and the central computer with each other toprovide full control of the testbed. Raw thermocouple data isconditioned by National Instruments data acquisition (DAQ)devices and sent wirelessly to the central computer. Wirelesscommunication with actuators is facilitated by National In-struments myRIO embedded hardware systems. These enablecontrol of the TECs (via PWM) and the ambient furnace(via on/off control) from the central computer. Note that allinteraction with the central computer is completely wireless.This is done for modularity and to more closely resemble therecently emerging wireless sensor network technology beingused in building management.

V. SOFTWARE ARCHITECTURE

All sensor data is transmitted wirelessly to a centralcomputer, which applies a control law and wirelessly commu-nicates control input to microcontrollers. Two parallel controlloops run simultaneously and independently: one to controlroom temperatures with any given control strategy, and oneto control the ambient temperature trajectory with on/offcontrol. This entire process is executed in NI Labview. Theoverall closed loop system can be operated at a sampling rateof 1 Hz, which is sufficient for building temperature control.To facilitate user-interaction, a GUI is created in NI Labview,which allows the user to control experiment setup options,ambient temperature trajectory, and room controller options.The GUI also utilizes a heat map to provide intuitive visualfeedback of all temperature sensors.

VI. SYSTEM MODELING

In this section, we create and experimentally validate aninput-output mathematical model of the testbed. Since wewish to accommodate testing of a wide variety of controllers,it is necessary to provide an accurate mathematical modelof the testbed which can be used to design controllers thatrequire model information. This model is constructed usingthe Building RC Model (BRCM) toolbox, developed bySturzenegger et al in [16], [17], which has been shownto have comparable fidelity to EnergyPlus simulations. TheBRCM model is based on a bilinear lumped Resistance-Capacitance thermal model given by:

x = Ax + Buu + Bvv +

nu∑i=1

(Bvu,iv + Bxu,ix)ui

y = Cx

(2)

Here, the complete state x is the temperature of rooms andwall layers (depending on the type of wall model used).A, Bu, and Bv are state space matrices determined by thetoolbox based on the building’s construction material, archi-tectural layout, and controlled variables. C is such that theoutput, y, corresponds to the measured (average) temperatureof each room. The variables u, nu, and ui refer to thecontrol input, number of inputs, and ith input, respectively.The system disturbances (such as ambient temperature andheat generation inside the building) are captured by thevariable v. Bvu and Bxu are coefficients of bilinear termsalso determined by the toolbox, and are used to model thenon-linearity associated with heat input (since heat input isdependent on the state x).

The BRCM toolbox uses a physics-based method fordetermining the model parameters (thermal resistance and ca-pacitance values) from building material properties, geometryand architectural drawings. The model for this testbed has atotal of 93 states: 6 room states, 57 wall temperature states(3 layers per wall segment), 24 ceiling temperature states (4layers per ceiling), and 6 floor temperature states. The outputy in this case corresponds to the 6 temperature states thatcapture the temperature of the 6 rooms. Since we have ex-plicit knowledge of building materials and wall constructions,

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we expect the simulation to match closely with experimentaldata. For validation of the model, the temperature plots fromsimulation of a decentralized P-type controller on the BRCMmodel are compared to the temperature measurements notedfrom experimental implementation on the testbed. Figure 6illustrates the comparison between the testbed performanceand the BRCM simulation respectively. We note that thetrends in temperature are comparable, although there is stilldiscrepancy between the model outputs and the measureddata from the testbed. It is key to reiterate that the BRCMis purely physics driven (white-box model) from materialspecifications and geometry of the testbed (i.e., it is not basedon any experimental measurement or system identification).Thus, it is expected that more accurate models can beobtained by tuning the physical parameters (such as wall R-values, heat transfer coefficients etc.) of the (grey-box) modelfrom experimental data fitting.

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Fig. 6. Comparison between experimental and BRCM simulation results.Room temperatures recorded experimentally under standard proportionalcontrol with a 22.2◦C setpoint, and then simulation is performed usinginput and disturbance data from the experiment.

VII. CONTROLLER EVALUATION EXAMPLE

This section presents a comparison of two different con-trollers. Figure 7 outlines the general control architecturefor the testbed. The controller generates the control inputs,ms and Ts (note that these are vectors which consist of theinputs to individual rooms), which results in a correspondingheat input, q, delivered from the TECs to each room. Thetemperature measured in each room (the average of allsensors within that room), y, is then fed back to the controller.In general, the controller C is multi-input/multi-output.

Fig. 7. Schematic of Controller Architecture

A. Decentralized Control Architecture

For the following sections, we will consider a decentralizedcontrol scheme, where the control input for each room isdetermined without knowledge of the inputs and outputs fromother rooms; i.e., we have 5 decoupled SISO controllers.Furthermore, to simplify the experiments in this section, weforfeit control of Ts by providing a constant duty cycle of1.0 to the Peltier device. Thus, the only control input ismass flowrate, ms. The choice to forfeit control of Ts forthese experiments aligns with many practical HVAC systems,where supply air is delivered at a constant temperature froma central air handling unit (AHU) and control is primarilyachieved by altering mass flowrate (i.e., by adjusting damperpositions, fan speeds, etc.). With these constraints in place,the decoupled control architecture is shown in Figure 8 for theith room. Notice that if the feedforward control is removed,this degenerates to a traditional feedback controller.

Fig. 8. Schematic of decentralized controller for the ith room. The supplyair temperature Ts is not used as a variable control input.

1) Decentralized PI Controller: For a PI controller, theheat input term can be given by:

q = −KP (y − ydes)−KI

∫(y − ydes) (3)

where KP and KI are diagonal gains.2) Decentralized Adaptive Controller: We next consider

the adaptive feedforward controller previously presented in[6], where the PI controller in (3) is augmented with afeedforward that depends on the ambient temperature, T∞:

q = −KP (y − ydes)−KI

∫(y − ydes) + F1T∞ (4)

˙F1 = −Γ1(y − ydes)T∞ (5)

where Γ1 is a positive definite gain matrix. In these experi-ments, the gains KP , KI , and Γ1 are hand-tuned based onsimulation.

B. Controller Performance Comparison

To evaluate these controllers, we run 2 separate trials inwhich we attempt to track a constant 21.7◦C setpoint inall rooms. The only difference between each trial lies inthe adaptive feedforward term. Figures 9a and 9b show theperformance of the PI and adaptive controllers, respectively.Notice that the PI controller overshoots in every room,substantially more-so in rooms 2 and 4. The adaptive con-troller does not overshoot, and maintains tight tracking ofthe setpoint once converged. In this experiment, one of the

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TECs in Room 2 is disabled to simulate the effects of anunder-actuated room. The results of this are easily observedin Figure 9, as Room 2 cools at a much slower rate thanother rooms. Figure 10 shows the ambient temperature forthese trials.

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Fig. 9. Comparison between PI and adaptive control. All 5 rooms arecontrolled to reach a 21.7◦C setpoint. Temperature values plotted reflectthe average temperature of all sensors within each room.

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Fig. 10. Ambient temperature during PI and Adaptive control experiments.

A significant feature of this testbed is the ability to com-pare energy usage under different controllers while holdingall other factors equal. In the experiment shown, the PIcontroller consumed a total of 3.35 MJ, while the adaptivecontroller consumed only 2.75 MJ (an 18% energy savings).

VIII. CONCLUSION

This paper presents the design and instrumentation ofa physical testbed to evaluate the performance of HVACcontrol algorithms for intelligent buildings. The testbed isenclosed in a temperature controlled environment, allowingthe operator to directly control and replicate the ambienttemperature trajectory. Thermoelectric coolers are used toprovide heat input to the testbed, and feedback is accom-plished via thermocouples connected to wireless DAQs. Asa preliminary demonstration, the testbed is used to comparetwo feedback control algorithms in terms of performance andenergy consumption. It is interesting to note that while aver-age temperature is regulated well with these controllers, thetemperature distribution within the room shows significantvariation, highlighting the importance of sensor placement.

Due to its scale, the testbed is unable to accommodatephysical occupancy. We intend to simulate the effects ofoccupancy by producing small (controlled) amounts of heatand moisture in each room. At the present, the testbed iscapable of temperature control only. Future work will includeintroduction of additional disturbances such as humidityand radiant sunlight through humidifiers and floodlights. To

accompany these features, sensors will be added to providehumidity and air quality measurements.

ACKNOWLEDGMENTThis work is supported in part by the NSF Award CNS-

1230687, the Center for Automation Technologies and Sys-tems (CATS) under a block grant from the New York StateEmpire State Development Division of Science, Technologyand Innovation (NYSTAR), and the Engineering ResearchCenters Program (ERC) of the National Science Foundationunder NSF Cooperative Agreement No. EEC-0812056.

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