MLE+: A Tool for Integrated Design and Deployment ofEnergy Efficient Building Controls∗

Willy Bernal, Madhur Behl, Truong X. Nghiem, Rahul MangharamDepartment of Electrical and Systems Engineering

University of Pennsylvania

{willyg,mbehl,nghiem,rahulm}@seas.upenn.edu

AbstractWe present MLE+, a tool for energy-efficient building au-

tomation design, co-simulation and analysis. The tool lever-ages the high-fidelity building simulation capabilities of Ener-gyPlus and the scientific computation and design capabilitiesof Matlab for controller design. MLE+ facilitates integratedbuilding simulation and controller formulation with integratedsupport for system identification, control design, optimization,simulation analysis and communication between software ap-plications and building equipment. It provides streamlinedworkflows, a graphical front-end, and debugging support tohelp control engineers eliminate design and programming er-rors and take informed decisions early in the design stage,leading to fewer iterations in the building automation develop-ment cycle. We show through an example and two case studieshow MLE+ can be used for designing energy-efficient controlalgorithms for both simulated buildings in EnergyPlus and realbuilding equipment via BACnet.

Categories and Subject DescriptorsI.6.3 [Simulation and Modeling]: Applications; D.2.2

[Software Engineering]: Design Tools and Techniques; J.7[Computer Applications]: Computers in Other Systems

General TermsDesign

KeywordsBuilding simulation, building control, control design, ener-

gyplus, energy-efficient building, integrated design, matlab

1 IntroductionIn the design of energy-efficient buildings, systems engi-

neers require both, tools capable of simulating high-fidelityplant models, and tools to design, evaluate and deploy mod-ern control methods on those plant models. There is currentlya significant gap between the functionality of the tools avail-able for building energy research. While simulation tools can

∗ This material is based upon work supported by the DoE Energy EfficientBuildings HUB sponsored by the Department of Energy under Award NumberDE-EE0004261.

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Buildsys’12, November 6, 2012, Toronto, ON, Canada.Copyright © 2012 ACM 978-1-4503-1170-0 ...$10.00

accurately simulate the behavior of a building and its energyconsumption, their capabilities for advanced control designand optimization are inadequate. This is, in part, is due tothe limited interaction between building modeling and simu-lation experts and the control systems engineers community.Consequently, as these research communities form tighter col-laborations, there is a need for software tools for end-to-enddesign of energy-efficient building control systems. This willnot only influence how architectural design choices directly af-fect the operational efficiency of building automation systems,but also facilitate the co-design of high-performance controlsystems for the specific building model. The focus of this ef-fort is on the development of MLE+, a co-simulation tool, thatutilizes the simulation capabilities of building energy softwaretools such as EnergyPlus while taking full advantage of theMatlab environment for control design.

Building simulation tools like EnergyPlus [1], TRN-SYS [2], ESP-r [3], eQuest [4], DOE-2 [5] and Design-Builder [6] offer powerful methods for simulating realistic be-havior of buildings and for evaluating their energy efficiencyand sustainability. These tools use high fidelity physical mod-els for heat conduction through surfaces, heat and mass trans-fer, coupling of air and water loops, thermal comfort, fenes-trations, daylighting control, weather conditions, atmosphericpollution, occupancy and Heating Ventilation and Air Condi-tioning (HVAC) equipment. Using increasingly detailed de-scriptions of a building, one can obtain an estimate of thebuilding’s energy requirements in terms of heating and cool-ing loads, interior environmental conditions and building au-tomation operation cost. Although building simulation toolscan run high-fidelity simulations, they have limited capabil-ity for algorithm development, optimization, control synthesisand model-based system design.

EnergyPlus [1] is one of the most robust building energyanalysis and thermal load simulation tools available today andit has become the de facto whole building simulation tool sup-

Figure 1. MLE+ interfaces control systems toolboxes withbuilding models and systems

ported by the U.S. Department of Energy. It is a stand-alonesimulation engine which processes text-based input files to runrealistic building simulations. It allows simultaneous simula-tion of loads, systems, and plant and therefore permits quickassessment of building performance. The most recent ver-sion also supports coupling with Functional Mock-up Units(FMUs) for co-simulation [7], which allows extending Ener-gyPlus with custom simulation code in the C language. It pro-vides a built-in energy management system that allows inte-gration of simple rule-based control.

However, EnergyPlus lacks the capability to directly inter-face with scientific computation and simulation software suchas Matlab and Simulink. Therefore, it is difficult to imple-ment and simulate advanced control feedback strategies suchas Model Predictive Control (MPC)[8], where an optimal con-trol signal is repeatedly computed based on the current state ofthe controlled plant. It requires considerable effort and timefor a control expert to obtain working knowledge of Energy-Plus, become familiar with its elaborateness and use it for im-plementing advanced control algorithms. Thus, we identifythe following desired capabilities of such a co-design and co-simulation tool:

• Support high fidelity building simulation software andstandard scientific computation software.

• Have the capability to compare and rapidly simulatescenarios of different control algorithm implementationsacross a range of building model parameters. This helpsin taking informed decisions during the early phases ofdesign.

• Facilitate common tasks in energy-efficient building de-sign, such as identifying and validating simplified modelsfrom high order physical models, optimizing parametersof a building model, designing advanced controllers andtheir quantative analysis for a building.

In this paper, we present MLE+, an open-source Mat-lab/Simulink toolbox for building energy research and devel-opment (Fig. 1). MLE+ provides the capability to perform co-simulation with EnergyPlus from Matlab. Co-simulation (orco-operative simulation) is a simulation methodology that al-lows individual components to be simulated by different toolsrunning simultaneously and exchanging information in a col-laborative manner. The following are the main features ofMLE+:

1. Simulation configuration: The MLE+ front-end stream-lines the configuration process of linking the buildingmodel and the controllers by abstracting the necessary pa-rameters from the co-simulation. This reduces setup timeand configuration problems.

2. Controller design: MLE+ provides a control develop-ment workflow as well as graphical front-ends for de-signing advanced control strategies, in which the buildingsimulation is carried out by EnergyPlus while the con-trollers are implemented in Matlab or Simulink.

3. Simulation-based optimization: MLE+ can be used tofind optimal parameters or control sequences for buildingsystem simulations in EnergyPlus.

4. Data analysis: After a co-simulation run, using MLE+,the output data from EnergyPlus can be aggregated, ana-lyzed and visualized in Matlab.

Figure 2. MLE+ tool interface

5. Building Management System Interface: MLE+ pro-vides a BACnet interface to develop and implement con-trol methods for real building equipment.

6. Matlab environment: MLE+ allows complete access tothe Matlab environment and toolboxes such as GlobalOptimization Toolbox, System Identification Toolboxand Model Predictive Control Toolbox. The user canstep through the code for debugging and pause the co-simulation at any time.

The rest of the paper is structured as follows. We firstpresent a running example of using MLE+ for designing a con-troller for co-simulation with EnergyPlus. Section 3 describesthe architecture of MLE+. Two case studies using MLE+ arepresented in sections 4 and 5. We conclude the paper with re-lated work in section 6, use cases of MLE+ in section 7 and adiscussion in section 8.

2 Using MLE+: A Simple ExampleTo explain the usefulness of MLE+, we use an example

of designing a feedback controller in MLE+ for actuating thewindow blinds in a building simulated in EnergyPlus.

A single-storied building shown in Fig 3(a) consists of threezones with a total floor area of 130m2. The West zone ofthe building consists of a large window equipped with blind-s/shades and is subject to strong solar radiation during theday. The goal is to control the window shade deploymentof the West zone such that the transmitted solar radiationthrough the window never exceeds a certain threshold. Thewindow blinds can be controlled using two EnergyPlus vari-ables: (i) Shading_Deployment_Status controls whether theblinds are deployed or not; (ii) ShadeAngle_Schedule con-trols the glare inside the zone and should be perpendicular tothe incident solar radiation whenever the blinds are deployed.We will design a controller in MLE+ which monitors the an-gle and intensity of the solar radiation incident on the Westzone window. If the incident solar radiation exceeds a certainthreshold, the blinds will be deployed and the shade angle willbe set to reduce the possibility of glare.

2.1 Environment ConfigurationTo start a new control development project, the simula-

tion environment needs to be configured. An EnergyPlus In-put Data File (IDF) and a weather file are first selected andparsed using the MLE+ front-end. For our example we usethe EMSWindowShadeControl.idf file, which is available asan example in the EnergyPlus distribution and contains the de-scription for our building.

2.2 I/O Variables ConfigurationMLE+ provides a graphical front-end for specifying the

input-output variables to be exchanged between EnergyPlusand Matlab for co-simulation. An input variable serves asan input to EnergyPlus at each step of the co-simulation,while output variables are those which can be repeatedlyread from EnergyPlus to monitor its internal state. Forour example, we specify Shading Deployment Status andShadeAngle Schedule as the inputs to EnergyPlus as theseare the variables that we will control using MLE+. The con-troller will need to monitor the incident solar radiation andangle at the West zone, therefore these are specified as out-put variables. After a description file for a building has beenloaded, the MLE+ front-end displays all available input andoutput variables pertaining to that building.

In MLE+, an alias can be specified for each of the variables(Fig. 3(b)). The alias allows a user to reference a variable witha more intuitive name and avoid the intricate names speci-fied by EnergyPlus. For instance, the EnergyPlus variableZn001_Wall001_Win001_Shading_Deployment_Statuscan be assigned a more intuitive name as ShadeStatus. Thealias of a variable can be used later during the controllerdesign and to map to addresses of physical devices in aBACnet network.

2.3 Control DesignIn this step, a controller for the window blinds is designed

and implemented. The MLE+ front-end for control designgenerates a Matlab code template for specifying the controlalgorithm based on the input-output variables defined in theprevious step. The I/O variables specified by the user are re-ferred to by their aliases throughout the control file as shownin Fig 3(c). In the code snippet shown in Fig 3(c) the value ofthe incident solar radiation is compared against the threshold(100 W/m2) to determine if the shades will be deployed.

2.4 Simulation and AssessmentOnce a preliminary control design has been agreed upon,

we can run the simulation or step through it using Matlab de-bugging environment. MLE+ successfully decouples the sim-ulation engine and the control strategy. This way we can workon tuning the control scheme from Matlab, running multiplesimulations without the need of modifying the EnergyPlus file.After the co-simulation finishes, MLE+ extracts and parses alloutput variables generated by EnergyPlus making them avail-able at the front-end (Fig 3(d)). The output variables can beplotted for a quick view and can be easily saved to the Matlabworkspace for further analysis.

Fig 3(e) shows the results of the window shade controllerfor our example. Notice how the shades are deployed when-ever the incident solar radiation exceeds a certain thresholdthereby limiting the transmitted solar radiation. Although theexample presented in this section is simple, it demonstrateshow MLE+ can expedite the control design by allowing theuser to quickly make changes to the controller, then simulateand assess the results.

3 MLE+ ArchitectureThe structure of MLE+, shown in Fig 1, consists of MLE+

Core components and the MLE+ Workflow. The MLE+ Corecomponents provide interfaces to building simulation tools andbuilding devices. The MLE+ workflow is a sequence of oper-ations which utilize the core components to efficiently design,

(a) EnergyPlus window shading control model

(b) Assigning aliases to variable names

mcode.sty Demo

Florian Knorn, florian@knorn.org

July 31, 2012

1 if Zone West Solar > 1002 % DEPLOYED WHEN SOLAR RADIATION EXCEEDS THRESHOLD3 ShadeStatus = userdata.Shade Status Exterior Blind On;4 ShadeAngle = IncidentAngle;5 else6 % SHADES NOT DEPLOYED7 ShadeStatus = userdata.Shade Status Off;8 ShadeAngle = IncidentAngle;9 end

10 % FEEDBACK11 eplus in curr.ShadeStatus = ShadeStatus;12 eplus in curr.ShadeAngle = ShadeAngle;13 end

1

(c) Matlab code snippet of the shading controller (notice alias variables)

(d) viewing plots of EnergyPlus outputs

1d 2d 3d 4d 5d0

1,000

2,000

3,000

Time

Rad

iatio

nkW

Window Incident Radiation (W)

Window Transmitted Radiation (W)

Shade ON

(e) Window shade controller results

Figure 3. MLE+ workflow example

simulate and evaluate a controller for a given plant model orbuilding automation platform.

3.1 MLE+ CoreThe MLE+ core handles the interfaces for data-exchange

with building simulation tools and communication with build-ing management systems. It uses a Java socket library forco-simulation with EnergyPlus and a BACnet stack library forcommunicating with BACnet devices. Currently, the interfacesupports communication with EnergyPlus and BACnet but in

Figure 4. MLE+ control development workflow.

future it will be extended to support other systems as well,for example the Radiance lighting simulation software [9] andOpenADR [10]. The MLE+ Core also provides an ApplicationProgramming Interface (API) which contains a set of low-levelMatlab functions and classes responsible for all other compo-nents the MLE+.3.2 MLE+ Utilities

MLE+ utilities are built on top of the API to facilitate thedevelopment of building energy simulation, control, analysisand optimization. Some examples of MLE+ utilities are:

• A function for parsing and extracting parameters fromEnergyPlus building description files.

• An editor that automates the configuration and mappingof external variables for EnergyPlus.

• A simulation result viewer that can load, plot, and exportsimulation results from EnergyPlus to Matlab.

• A drawing viewer that exports the building geometryfrom EnergyPlus and display it in Matlab.

3.3 MLE+ Simulink blocksMLE+ provides a Simulink blockset library for co-

simulation between Simulink and other building simulationsoftware. The Simulink blocks are essentially a wrapper of theMLE+ Core for Simulink. This facilitates model based designfor building energy control.3.4 MLE+ Workflow

The core components of MLE+ enable efficient workflowfor common tasks, such as developing advanced building con-trols and optimizing building automation parameters. The de-velopment workflow, as shown in Fig 4, facilitates a completedesign cycle from model identification of an EnergyPlus build-ing model, to designing an energy-efficient controller, to fi-nally deploying the control algorithm in real buildings throughBACnet.

4 Case Study 1:Energy-Efficient Controller Design

Radiant heating systems serve as an alternative to the con-ventional forced-air heating, ventilation and air conditioning(HVAC) systems for thermal conditioning of buildings. A ra-diant floor heating system works by warming up the floor sur-face which then slowly radiates heat upward into the livingspace. In [11] an algorithm for peak power reduction of ra-diant heating systems is presented and is implemented usingEnergyPlus. For this case study, we will use the same exampleas in [11]. The objective here is not to develop new energy-efficient control algorithms for radiant heating systems, but to

Table 1. List of parametersRzi , j thermal resistance of the jth layer in zone ziCzi , j thermal capacitance of the jth layer in zone ziCzi thermal capacity of zone ziKzi thermal conductance between the zone and outside airKi j thermal conductance between the zone i and zone j, i 6= jTa outside ambient air temperature

Qhg,zi internal heat gain due to occupants etc.Qsol,zi heat gain due to solar radiation etc.Qrad,zi heat gain from radiant floor system etc.

show the potential and ease of use of MLE+ in developing andevaluating such algorithms.

4.1 Simulation set-upA single floor, L-shaped building divided into

three interior zones is used as the plant model. TheRadLoTempElecTermReheat.idf description file providedwith EnergyPlus examples was used for this case study. Anelectric low temperature radiant system is used for heatingthe floor of each zone, with power ratings of 12kW, 8kWand 8kW for the North, West and East zones respectively.Temperatures in each zone were required to be kept betweenl = 22 ◦C and h = 24 ◦C. The ambient air temperature profilewas of Chicago, IL, USA. We will compare two differentscenarios. In the first one, we used MLE+ to implement acontrol strategy to switch the electric radiant system in eachzone ON or OFF (binary) in an energy efficient manner whileensuring that thermal comfort inside each zone is maintained.The second scenario considers a continuous control wherethe controlled variable takes any number between 0 andthe maximum output power of the radiant system. For thiscase, we will use MPC to minimize the total energy usagethroughout the day.

4.2 Model IdentificationThe internal thermal model of the EnergyPlus building is

not accessible from outside EnergyPlus, therefore the first stepwas to identify a linear state-space model for the building usingthe MLE+ system identification workflow.

4.2.1 System ModelThe dynamics of each zone is modeled using a RC network

“lumped-parameter” model as shown in Fig 5. The list of allparameters in the model is given in Table 1. The law of conser-vation of energy gives us the following heat balance equation

Tz1,0

Ahg

1Tz1,1

Tz1,i

Tz1,m1

source,z1

Cz1,1Cz1,1

Rz1,1

Cz1,iRz1,i

Floor Surface

Tz1

Aha

1Cz1

K12

Ahg

1Tz2,1

Tz2,i

Tz2,m1

source,z2

Cz2,1Cz2,1

Rz2,1

Cz2,iRz2,i

Tz2

Aha

1Cz2

Tz2,0

Floor Surface

Flo

or L

ayer

s

Ta Ta

Qrad,z1 Qrad,z2

Kz1 Kz2

Ground

{Qhg,z1;Qsol,z1} {Qhg,z2;Qsol,z2}

Figure 5. Thermal RC network model for an electric radi-ant floor heating system

for zone zi:

Czi

dTzi(t)dt

= Kzi (Ta(t)−Tzi(t))+∑j 6=i

Ki j(Tz j(t)−Tzi(t)

)+Qhg,zi(t)+Qsol,zi(t)+Qrad,zi(t) (1)

where Tzi is the temperature of zone zi. The control input toeach zone zi is the state of the electric radiant system. Thiscontrol input is denoted by ui ∈ [0,1] where ui = 0 correspondsto the OFF state with no power consumption and ui = 1 theON state with maximum power consumption and any interme-diate value of u denotes the fraction of maximum power beingconsumed. Differential equations for all the zones can be com-bined to give the following state space model for the building:

x(t) = Aix(t)+Bu(t)+Dw(t) (2)

Where the state x(t) consists of node temperatures (3 nodesper zone), control input u(t) to the radiant heater, and w(t) aredisturbances to the system (solar heat gain, occupants etc.).4.2.2 System Identification with MLE+

System identification involves using time-domain andfrequency-domain input-output data to identify continuous-time and discrete-time transfer functions, process models, andstate-space models. The MLE+ system identification work-flow helps in (i) Generating input-output data from EnergyPlusand (ii) Structuring input-output data in a format ready to beused by Matlab’s System Identification toolbox.

The MLE+ system identification front-end can generate sig-nals with distinct characteristics for identification purposes.Fig 6(a) shows how the control inputs to EnergyPlus are ini-tialized as random binary signals (rbs) to carry out controlledsimulations in EnergyPlus. The MLE+ system identificationmodule streamlines the process of packaging the simulated I/Ovariables into a Matlab object. This object can be importedinto the Matlab’s System Identification Toolbox to identify theparameters of the radiant system (Fig 6(b)). This particular ex-ample demonstrates how MLE+ allows a seamless integrationwith Matlab Toolboxes and built-in functions.4.3 Energy-Efficient Controller Design

In the ON/OFF case, uncoordinated operation among elec-tric radiant heating systems across multiple zones can cause

peaks in the electricity consumption of the building. This oc-curs when all electric radiant heating systems are simultane-ously consuming electricity i.e., the electric radiant system ineach zone is ON at the same time. Our goal is to evaluatedifferent control methods to reduce the peak power consump-tion while ensuring that the temperature inside each zone stayswithin the comfort range. Through MLE+, one can specify anyvalid control method for a plant, since it provides a templatecontrol file as starting point. We implemented two strategiesfor this scenario in MLE+:

1. On-Off uncoordinated control: To observe uncoordi-nated control among the zones, we implemented a base-line controller in MLE+ to regulate the temperature ofeach zone like a two-position thermostat.

2. Green Scheduling for peak power minimization: Us-ing the identified state-space model, we implemented theGreen Scheduling algorithm for peak power reductionof radiant heating systems in MLE+. Green Schedul-ing [11], is a simple and lightweight approach for coordi-nating energy consuming control systems to reduce theirpeak power consumption.

In the second case, we implemented two continuous controlschemes to maintain the zone temperatures within the samecomfort range.

1. Proportional control: This simple control feedback ispurely reactive as it only considers the current zone’stemperature values. The controlled variable, the electricpower of the radiant system, is proportional to the differ-ence of the setpoint and the zone’s temperature.

2. Model Predictive Control: Using MLE+, we imple-mented a (relatively sophisticated) model based predic-tive controller for total power minimization. We used theidentified linear state-space model with Matlab’s MPCtoolbox to compute the optimal power level of the elec-tric radiant heater in each zone. We then fed these powerlevels as control inputs to EnergyPlus via MLE+ at eachsimulation step.

By designing multiple controllers in MLE+ for the sameplant in EnergyPlus, we can make a fair comparison betweenthe performance of the control algorithms for each scenario.

(a) Generating input-output data for system identification

(b) Packaging input-output data System Identification

Figure 6. System Identification using MLE+

8a 9a 10a 11a 12p 1p 2p 3p 4p 5p 6p

10

20

30

Time of day (h)

Pow

er(k

W)

Green Scheduling Uncoordinated

(a) Green Scheduling vs. Uncoordinate Power Consumption

8a 9a 10a 11a 12p 1p 2p 3p 4p 5p 6p

10

20

30

Time of day (h)

Pow

er(k

W)

MPC Proportional

(b) MPC vs. Proportional Power Consumption

Figure 7. Case Study 1 Power Profile

The power profile of the four control methods are shown inFig 7(a) and 7(b) while total power consumption and peakpower are presented in Table 2. In both cases, zone tempera-tures were kept in the desired range between 22◦C and 24 ◦C.We observed that the curve of electricity demand for the unco-ordinated control strategy had several high peaks while it wassmoother and flatter for the green scheduling strategy. In total,green scheduling helped save 8% in electricity consumptionand reduce peak demand by 42.9%. Also, there is a decrease inthe total energy consumption since the periodic schedule tendsto operate at a lower mean temperature than the uncoordinatedcontrol.

For the continuous control case, the MPC simulation didnot achieve a smaller total power during the day. This outcomecan be attributed to the fidelity of the model. The MPC imple-mentation requires a more rigorous model and higher accuracyin predicted parameters. These improvements are definitelyachievable with MLE+. We did not pursue this task, as ourintention was not focused on the development of a novel feed-back control, but on the potential of MLE+ to achieve this. Inthe control design iteration process, MLE+ proves extremelyuseful as it allows access to the complete debugging environ-ment within Matlab. This translates into considerable time andeffort savings.

5 Case Study 2:BACnet interface with test-bed

Controllers implemented in MLE+ can be used for control-ling real building devices through BACnet. BACnet [12] wasdesigned to allow communication of building automation andcontrol systems for applications such HVAC, lighting control,access control, fire detection systems and their associated de-vices. The BACnet protocol defines the format and delivery ofmessages these devices exchange.

ON-OFF Control Continuous ControlUncoordinated GS (%saved) Proportional MPC (%saved)

Consumption (kW h) 93.2 85.7 (8.0%) 84.5 99.7(−2.8%)Peak demand (kW) 28.0 16.0 (42.9%) 17.4 17.9 (−15.3%)

Table 2. Consumption and peak demand in case study 1

5.1 The BACnet standardBACnet provides a way of representing any device as long

as the device has certain functions specified in the BACnetstandard. The standardized BACnet model of a device rep-resents these functions as collections of related informationcalled objects each of which has a set of properties that fur-ther describe it. Each analog input of a device, for instance,is represented by a BACnet Analog Input object which has aset of standard properties like Present Value, Sensor Type,Location, Alarm Limits, and so on. Some of these proper-ties are required while others are optional. One of the object’smost important properties is its identifier, a numerical namethat allows BACnet to unambiguously access it.

An example of a BACnet device is a temperature sensor.It is represented as a BACnet Analog Input object with aPresent Value property which is read as the sensor signal.

5.2 MLE+ BACnet interfaceThe BACnet interface in MLE+ (Fig5.2) is built upon the

BACnet Stack [13] , which is an open source BACnet protocolstack for embedded systems. For the case study, MLE+ is in-terfaced with a test-bed that simulates both the dynamics of abuilding and the behavior of BACnet devices.

The test-bed consists of a building with four zones, asshown in Fig 5. Each zone has independent heating and cool-ing elements which can be controlled to regulate its tempera-ture. Sensor nodes monitor the temperature and energy levelsin different zones of the building. An energy dashboard, shownin Fig.8(b), displays the current temperature of each zone andthe power consumption of the building. The test-bed is iso-lated from the surroundings by an outer box, which acts as theoutside environment for the building. Fig. 8(b) shows that thetemperature dynamics of each zone in the test-bed is similar tothe dynamics of a real building.

Each zone of the building test-bed acts as a single BACnetdevice with two objects: (i) An Analog Input object to readthe zone temperature, and (ii) An Analog Output object tocontrol the status of the heating element in the zone.

BACnet is based on a “client-server” model, thereforeBACnet messages are often called service requests. MLE+ be-haves as a client and sends service requests to a server, whichis usually a BACnet Device. The device/server then performsthe service and reports the result to MLE+. A common servicerequest is the Who-Is broadcast message, which help the clientto identify devices on the BACnet network. MLE+ can gener-ate Who-Is service requests to identify the BACnet devices inthe building (Fig9(a)). Each device replies back with an I-ammessage containing its device ID. MLE+ can identify the listof all available BACnet devices or devices with IDs that fall ina specific range. MLE+ can also query a specific device if itsID is known. This is useful for checking whether the device isstill operational.

Based on the functionality of a device, MLE+ can readand write properties to it. Fig 9(b) depicts how the PresentValue of the Analog Output of zone 1 can be set to 10by sending a BACnet WriteProperty request. The AnalogOutput object controls the heating element of the zone and avalue of 10 that the heating element will operate at 10% of itsmaximum operating power. Similarly, as shown in Fig 9(c),using the Read Property request, the temperature value of azone can be read. The temperature sensor is represented as anAnalog Input object.

(a) Test bed with 4 zones (b) Energy dashboard showing zone temperatures and power

Figure 8. Building test-bed with BACnet connectivity to MLE+

Since MLE+ allows designing controllers in Matlab, a sim-ple feedback controller can be specified for each zone. Thecontroller periodically reads the temperature from each zoneand switches the heating elements ON and OFF such that thetemperature inside each zone stays within the upper and lowerthresholds at all times.

This is a simple example but it paves the way for usingMLE+ for modern energy-efficient control methods such asMPC, as described in Section 4). Fig 8(b) shows the perfor-mance of two controllers implemented for the test-bed withMLE+. Controller 1 controls each zone independently of theother zones. This uncoordinated behavior among the zones re-sults in peaks in the total power consumption of the building.Controller 2 coordinates the zone controls such that the peakpower consumption for the building is minimized.6 Related Work

The building automation communities have explored theapplication of advanced control methods for buildings [14, 15,16, 17, 18] using building energy simulation tools. In [14], theperformance of a real building is analyzed and optimized us-

(a) BACnet Who-IS request in MLE+

(b) BACnet Write Property in MLE+

(c) BACnet Read Property in MLE+

Figure 9. MLE+ BACnet Interface

ing EnergyPlus models. In [18], a Siemens Apogee controlleris used for demand response control and predictive modelingof a campus building using EnergyPlus. [14, 15, 17] presentoccupancy driven approaches for energy efficient building con-trol but the proposed control methods cannot be implementedin EnergyPlus since EnergyPlus uses fixed schedules for occu-pants. In each of these cases, MLE+ can be used for perfor-mance evaluation of the proposed control methods with Ener-gyPlus.

HAMlab [19] provides a collection of tools suitable for sim-ulation with Matlab/Simulink but is mainly suited for mod-eling the heat and moisture flows in a building. THER-MOSYS [20] is another Matlab toolbox for analyzing the be-havior of air-conditioning and refrigeration systems. It con-tains dynamic models of the basic components used in com-pression cycles but it cannot be used for whole building simu-lation. TRNSYS can as well interface with Matlab by runningit as a process for simulating a design component. However, itdoes not provide whole-building simulation and does not ex-ploit Matlab’s environment. Specifically, it cannot use Mat-lab’s built-in debugging capabilities.

A popular tool for building energy co-simulation is theBuilding Controls Virtual Test Bed (BCVTB) [21], but it has afew limitations which are discussed next.6.1 Comparison with BCVTB

BCVTB is a software environment, based on Ptolemy II,for coupling different simulation programs [22, 23]. It can linkdifferent simulation programs for co-simulation, including En-ergyPlus and Matlab. The co-simulation feature in EnergyPluswas originally developed for BCVTB and can be used by anyprogram to perform co-simulation with EnergyPlus. MLE+ isan example of such a program.

Although Matlab can be coupled with EnergyPlus viaBCVTB, its full functionality cannot be used because Mat-lab is only called by BCVTB as an executable client. There-fore, interactive execution and debugging of Matlab code isnot possible. Furthermore, if the Matlab code or the Simulinkmodel has an error, it is much more difficult to find and fixit with BCVTB than with MLE+, which runs in the standardMatlab environment. For users who mostly work with Mat-lab/Simulink and have never used Ptolemy, learning a new en-vironment as Ptolemy is time-consuming.

These limitations of BCVTB are echoed in the case studypresented in [24]. In the study, the authors acknowledged thatBCVTB requires considerable know-how and effort in order toset up and operate the co-simulations. They also state how de-bugging became more difficult for them as adding breakpointsin Matlab code disrupted the co-simulations. They also experi-enced an increase in the simulation times when using BCVTB.

MLE+ has some distinct advantages over BCVTB:1. It takes full advantage of the Matlab/Simulink environ-

ment, including interactive simulation, code debugging,code generation, and all available toolboxes. In otherwords, it integrates better with Matlab/Simulink.

2. It is easier to extend MLE+, using Matlab programming,for specialized applications and functions.

3. It is more familiar to users who mainly use Mat-lab/Simulink, i.e. control engineers.

7 Use casesMLE+ has been successfully used by both industry and

academia ( [25, 26, 27, 18]). In [25], Siemens implemented anMPC-based Energy Management Controller (EMC) in Matlabthat exchanged data with EnergyPlus via MLE+. The authorsclearly assert in their work how they were able to take fulladvantage of the Matlab toolboxes and MLE+ to achieve thisco-simulation. In [18], an intelligent automated demand re-sponse building management system using EnergyPlus is pre-sented. According to the report the team explored the utiliza-tion of BCVTB but faced simulations problems caused due tocommunication between EnergyPlus and Matlab. They thenswitched to MLE+ for developing the demand response strate-gies. The authors state that MLE+ is a much more reliableway to synchronize the simulation between Matlab and Ener-gyPlus. Another MPC implementation for EnergyPlus usingMLE+ is presented in [27] .8 Discussion

While building simulation software tools can carry out ac-curate and realistic building simulations they only provide verybasic control methods. On the other hand, control engineersand researchers have explored advanced control strategies forenergy-efficient operation of a building, but more often thannot, such methods are based on simplified physical models in-stead. MLE+ is intended to be used as a tool for building en-ergy research and development by researchers who are familiarwith Matlab and want to use realistic building simulation capa-bility of building energy simulation software like EnergyPlus.

Being a Matlab toolbox, MLE+ gives the user completeaccess to all the toolboxes in Matlab and its computationalpower. The ability to pause the co-simulation with EnergyPlusand step through the code for debugging saves precious devel-opment and testing time. This helps control engineers to rec-ognize problems, eliminate errors and take informed decisionsearly on in the design stage leading to fewer iterations in thedevelopment cycle. This feature is not available in tools likeBCVTB. Workflows in MLE+ make it very easy to managetasks like model identification, control design and optimiza-tion, post simulation analysis and integration between softwareapplications and building equipment. MLE+ can also be usedfor tuning building parameters for both building simulationmodels inside EnergyPlus and for real building equipment.

Our current effort involves extending the tool to work withother building energy simulation tools like Radiance and Ope-

nADR and optimization and modeling tools such as BLOM[28]. Our future work includes using MLE+ for synthesizinga control strategy to control the indoor illumination levels andHVAC system based on predicted and real time solar incidentradiation measurements in real buildings.9 References[1] D.B. Crawley, L.K. Lawrie, C.O. Pedersen, and F.C. Winkelmann. En-

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[28] BLOM: The Berkeley Library for Optimization Modeling and NonlinearModel Predictive Control. http://www.mpc.berkeley.edu/people/sergey-vichik/file/BLOM_paper.pdf.