A Simplified Rapid Energy Modeland Interface for Nontechnical Users

Bryan J. Urban Leon R. Glicksman, PhDMember ASHRAE

ABSTRACT

When used early in the design process, simulation can help designers reduce a building’s energy demand. Early-stage simu-lation must allow the user to explore many options quickly. Most building simulation programs have been structured for modelingfinalized building designs very accurately. Intricate detail, often including CAD models, must be entered before such simulationscan take place. This can take hours or days to prepare, which is not useful for early-stage design iteration. Further, most simulationtools require a technical background and substantial user training before they may be used effectively. By simplifying the modelingprocess, and specifically the user-interface, it is possible to make simulation tools accessible to a wider audience. The MIT DesignAdvisor is presented as means for non-technical specialists to rapidly simulate and compare energy consumption of early-stagebuilding designs. An overview of the simplified interface and the rapid energy model are given, and sample validation cases areexplored. The MIT Design Advisor is available at http://designadvisor.mit.edu.

INTRODUCTION

In this paper a case is made for a new category of simu-lation tools that are appropriate for nontechnical buildingdesigners. One such tool, developed by the authors, is thenpresented. A description is given of the simplified user inter-face, which has been condensed to a single page of basic build-ing parameters. Next, an overview is presented of the energy-load simulation procedure. Finally, validation cases are madefor key components of the model, showing agreement withaccepted tools.

A NEW CATEGORY OF DESIGN TOOLS

The art of building simulation is well established andexperienced professionals can compute building loads withpractical confidence. Building simulation packages are in noshort supply. The motivation for creating yet another class ofsimulation tools is to combat the excessive complexity thatmakes many tools inaccessible to building designers, archi-tects, and engineers for early stage design comparisons. Build-ing simulation is typically deferred to post-design analysis.

Consequently the simple, inexpensive opportunities for reduc-ing energy consumption in buildings are often overlooked.Simplified approaches in both interface design and modelingtechnique can help empower the non-technical designer tomake better design choices.

Building Simulation Engines

Because of their extensive feature-set, building simula-tion engines such as Energy Plus and DOE-II lend greatmodeling flexibility to the experienced technical user. In thisclass of software usability is typically compromised in favor ofincreased ability to model complex building scenarios. Manyunderlying assumptions are made without being explicitlyevident to the casual user. Users must have a detailed under-standing of the simulation process to make useful designcomparisons.

To compound the issue, most of these engines lack a pre-packaged simplified user interface. Inputs must be prepared aslarge text files fraught with technical jargon, physical proper-ties, and programming constructs. Preparing input files can be

© 2007 ASHRAE.

Bryan Urban is a research assistant and PhD candidate in mechanical engineering and Leon Glicksman is a professor of mechanicalengineering and architecture at the Massachusetts Institute of Technology, Cambridge, MA.

a daunting task. Finally, simulation engines usually contain nointerface for making comparisons. Raw output data must beinterpreted by the user, which can take time and introduceerrors. Consequently the most powerful simulation tools areused only by experienced consultants and only then to simu-late finalized or near-finalized building designs.

Third-Party Interfaces

A number of third-party user interfaces have been devel-oped for use with simulation engines. Front-end applicationssuch as eQUEST and BLAST make the creation of input filesand the interpretation of results accessible to more users.While such tools are helpful many still fail to address the needfor an interface suited to the non-technical user. These inter-faces often barrage the user with many questions about veryfine details. While such details are necessary for simulating afinalized design, they may preclude simulation for exploringdesign options. The schematic design wizard feature ofeQUEST, shown in Figure 1, requires some 41 screens of user-input before even a basic configuration can be modeled.

Many building certification programs require a detailedcomputer analysis of building energy performance to gaincredit for a design (LEED, ASHRAE 2001). Although time-consuming and possibly expensive, preparing simulationsonce for a completed design may be appropriate. Such simu-lations, however, are unlikely to uncover great opportunity forsignificant energy reduction, since most of the design deci-sions will have already been made.

Simplified Engines and Interfaces

The motivation for a new design tool follows from theexcessive complexity of existing design tools. Reasonablyaccurate results are attainable without burdening the user withexcessive detail or complexity. To the designer it is moreimportant to know the relative energy-impact of possibledesign options than it is to know exactly how much energy avery specific building will consume.

At the early design stages, vast time resources are typi-cally not available. Full-scale simulations are unrealistic whenfew parameters have been decided upon. If architects are to usesimulation, a quick, simple, and iterative exploration of thedesign space is required. The MIT Design Advisor offers away for non-technical users to evaluate the energy perfor-mance of a preliminary building and to graphically comparethe results of up to four buildings simultaneously. Revising abuilding design and re-running the simulation can be done ina moment, and in all cases, the simulation provides results inless than one minute. Results include hourly heating, cooling,and lighting loads throughout the year, thermal comfort data,and daylighting information.

The primary goal of this software package is to assist thearchitect in quickly discovering which design factors can savethe most energy while maintaining occupant comfort. Energysavings of 15% or better may often be realized by altering abuilding’s orientation, selecting efficient windows, or adjust-

ing the ventilation rate. By making the powerful modelingtools and techniques available to the non-technical commu-nity, it may be possible to promote better design.

MODEL STRUCTURE

We now describe the simplified user-interface and model-ing technique of the MIT Design Advisor tool. Modeling isaccomplished with several independent load-computingmodules that are outlined in this section and covered morefully in later sections.

User Inputs—A Minimalist Approach

An interface with appropriate sophistication is critical. Asimplistic and functional interface is described. When techni-cal details must be specified, drop-down menus provide text-based clues indicating reasonable values for typical buildingconfigurations. Table 1 shows the minimum input required fora simulation.

All inputs are entered on a graphical setup page, seeFigure 2. Nominal values for most parameters are pre-selectedas defaults. Experience with students indicates that a new usercan master the main elements of the program in less than onehour. Advanced users can modify additional options for amore sophisticated simulation. These include thermostatsettings, air change rates, lighting control schemes, blind typesand settings, and advanced façade configurations includingairflow windows. The casual user is free to ignore theseoptions and typical values are used in their place.

Simulation Overview

When the user has specified the basic parameters, a fastenergy simulation is conducted. See Figure 3 for a softwarelogic diagram. A library of Typical Meteorological Year(TMY2) weather data files have been generated using theMETEONORM software. A weather data file is retrievedbased on a user-specified city. Outdoor temperature, windvelocity, and solar data are read for each hour of the year.Using solar data and geometry, a daylight module is used onceper hour to compute light intensity levels throughout the room.The required amount of artificial light is then computed basedon the user-selected lighting control system. Multiple reflec-tions of daylight are considered between interior surfaces. Anew algorithm (Lehar 2004) was developed to allow accuratesolutions to be obtained an order of magnitude faster thanprograms like the Lawrence Berkeley National Labs (LBL)Radiance software.

Energy exchanges occurring from internal loads, façadeheat exchange, ventilation, and thermal mass effects are usedto compute heating and cooling loads for each zone. Resultsare compiled, normalized by floor-area, and returned graphi-cally to the user. Monthly and annual heating, cooling, andlighting loads are reported, and sample output graphs arepresented in Figure 4a. Levelized annual energy costs aregenerated by specifying the cost of electricity and heat, and a

2 Buildings X

Figure 1 Page 1 of 41 in the eQUEST schematic design wizard. A daunting amount of information is required to prepare asimulation for even the simplest designs.

Table 1. Basic Input Options

Inputs Description

Zone configurationOne zone confined to a single-side of the building;Four-sided building with well-mixed air; orFour-sided building with air unmixed between zones

Building Location: select the nearest city for climate dataBuilding dimensions: NS and EW lengths

Room Primary façade orientation: N,S,E, or WRoom dimensions: depth, width, and height

Window Type: single-, double-, triple-glazed or double-skin façadeCoating: clear, low-e, etc.Glazed area: as percentage of wall area

Wall Specify: low, medium, or high insulation

Thermal mass Specify: low, medium, or high thermal mass

Occupancy

Occupant density: # people per floor areaEquipment load: watts per floor areaMin. lighting req.: lux Occupancy schedule: hours of occupancy

Ventilation Mechanical system;Natural ventilation only; ORHybrid mechanical and natural ventilation

Buildings X 3

4 Buildings X

Figure 2 A portion of the single-page MIT design advisor graphical interface. Up to four building scenarios can be saved andretrieved quickly from the scenario boxes.

Figure 3 Block diagram of software logic.

Figure 4 Sample load comparison and energy cost for four building designs.

(a) (b)

suitable cash discount rate, see Figure 4b. Thus, a financialcase can be made for installing more efficient components.

Computing heating and cooling loads independently oflighting loads can produce incorrect results since artificiallights contribute heat to the space. When blinds are adjusted byoccupants to reduce glare, both daylight and the solar heatgains are varied. An integrated model is necessary to capturethe interplay between all building control systems.

ENERGY BALANCE

Energy exchanges occur via several channels: internalloads, ventilation, envelope, thermal mass, and HVAC – seeFigure 5. Internal loads depend primarily on the occupancyconditions in the building; it is assumed that internal loads areconstant during any given hour. Ventilation and envelope loadsare proportional to the indoor-outdoor temperature difference,and their magnitude can vary modestly within a single hour asthe room’s air temperature floats between the upper and lowerthermostat bounds. Thermal mass loads are more compli-cated: variation depends on the instantaneous indoor temper-ature, the incident heat flux, and the temperature distributionwithin the mass itself.

One way of solving large thermal systems is to assigneach surface a temperature node, generate a system of energybalance equations, and use matrix inversion to solve thesystem of equations. Because some of the heat transfer coef-ficients are non-constant, it would be necessary to frequentlyre-invert the large matrix, and this is impractically slow.

Instead for each room, all of the loads Qi (in units of W)are computed independently for the various channels of energyexchange. The computations are made only as frequently asneeded: once per hour for internal loads, and 5 to 10 times perhour for envelope, thermal mass, and ventilation loads. Herewe outline how each of the four types of loads is computed andshow how they can be used to predict HVAC loads.

Internal Loads

Internal loads Qint consist of the sum of heat generated byequipment, artificial lighting, and people. These valueschange throughout the day based on occupancy schedules,available daylight, and lighting control strategy. Each internalload is modeled as a constant during any given hour. Occu-pants provide a constant source of energy; 60W of sensibleheat is typical for an average person sitting in an office.

Envelope and Solar Gains

Energy exchanges through the building envelope includedirectly transmitted radiative gains through window elements,and convective/conductive gains through windows, walls, andother insulating members. Instead of using tabular U-Valuesand Solar Heat Gain Coefficients (SHGC), which are typicallyonly appropriate for harsh design conditions, heat transfercoefficients are computed dynamically based on environmen-tal conditions. This is important because radiation coefficientsvary with material temperature and because spectral properties

of glazing elements vary with angle of incidence. Variation inradiation coefficients are computed using a linearized radiationheat transfer coefficient. Angular-spectral variation is deter-mined via the Fresnel equations (ASHRAE 2005, Siegel).

Transmitted solar gains include the portion of sunlightreaching the room interior. TMY2 weather data files supplyvalues for direct-normal and diffuse-horizontal radiation flux.These values are converted to incident values on a verticalsurface using the solar geometry calculations outlined inASHRAE (2005). Each hour the transmitted fraction of inci-dent radiation is computed. Multiple reflections are consid-ered to determine the energy absorbed, reflected, andtransmitted by all elements of a multi-layered façade. Spec-trally-selective materials are modeled with a tri-band radiationmodel: IR, visible, and solar-thermal radiation are eachconsidered separately. The thermal-circuit method shown inFigure 6 is used for computing gains through the windowsQwindow and walls Qwall. Thermal mass effects are assumednegligible for envelope components, and a steady-state modelis used.

Radiant interactions between blind, window, and roomsurfaces are computed using a radiosity method, and thetemperature of each surface is computed at every timestep.Double skin façades and airflow windows, depicted in Figure7, can be modeled in a similar manner, though an additionalmass-flow energy balance must be computed. The air flowingthrough the window cavity can exchange energy by convec-tion with the window pane and with the blinds. Since thetemperature can vary significantly in the vertical direction ofsuch windows, the cavity is discretized into 5 to 10 verticalslices. A thermal circuit, similar to the one shown in Figure 6is solved repeatedly, beginning with the lowest slice and work-ing upwards. Supply windows of this kind can introduce freshair into a room via a vent at the top of the window. In such casesthe temperature of the air at the cavity exhaust is computed andused in the ventilation load computation.

Thermal Mass

It is assumed that the building’s thermal mass is concen-trated in the floor of each room. The amount of thermal mass– low, medium, or high – is determined by the thickness (0.02,0.10, or 0.20 m) of a slab of concrete which makes up the floor.

Solar energy transmitted through fenestration isassumed to be evenly distributed over the surface of the floor.A fraction, equal to 80% is absorbed at the floor surface, andthe remainder is reflected to other surfaces in the room –walls, objects, etc. An approximation is made that roomsurfaces other than the floor and the exterior wall/windowexist at the room’s air temperature. Since the interior wallshave a moderately high internal conduction resistancecompared to surface convection, most of the radiationabsorbed by these surfaces will ultimately reach the air byconvection. Thus as a first approximation, the transmittedsolar radiation that is not absorbed by the floor is addeddirectly as a thermal load to the air.

Buildings X 5

For very low-mass floors a lumped-capacitance approachis fast and reasonably accurate. Higher mass floors that cansustain a vertical temperature distribution require a depth-discretization to compute thermal response. A conservativehorizontal slice thickness can be selected by ensuring thesurface node satisfies the Biot relation

(1)

This minimum slice thickness dmin is then used as the thick-ness for all layers. Similarly, a maximum stable time step tmaxis determined using the Fourier relation

(2)

For the depths listed earlier the minimum number of slicesmay be as high as 25 and the maximum time step for an explicitsolution is on the order of 30 seconds to 1 minute.

Using values of tmax and dmax a convection/conductionenergy balance is performed on each node of the discretizedslab floor, as shown in Figure 8. Using a combined surfaceconvection and IR-radiation heat transfer coefficient h=8.0 W/m2-K, and one-dimensional conduction through the depth ofthe thermal mass, a basic system of linear heat balance equa-tions is constructed in matrix form. This matrix is invertedonce and used repeatedly throughout the annual energy simu-lation to update the temperature distribution and to computehow much energy is exchanged with the air via convection.The energy traveling from the thermal mass surface to the airQtm is computed as the sum of convected energy and reflectedsolar energy.

Since constant coefficients are used, the matrix is invertedonly once and it is computationally inexpensive to frequentlycompute the temperature distribution in the thermal mass.When using an extremely conservative t of one minute over ayearly simulation, calculation time for thermal mass responseis less than 5 seconds on a modern desktop computer.

Ventilation and HVAC Loads

Ventilation exchanges consist of mass exchange with theoutside environment. Fresh air is introduced to the room at theoutdoor temperature and mixed with the indoor air, while indoorair is exhausted to the outside environment at the same rate.

In mechanically ventilated systems the flow rate is linkedto the occupancy to establish safe levels of fresh air. Typicalventilation rates range from about 2.5 to 10 L/s-person fornon-smoking and about 40 L/s-person for smoking areas(ASHRAE 62.1-2004). As the ventilation energy is linked tothe time-varying room air temperature, it must be re-computedseveral times per hour.

When natural ventilation is permitted, a crossflow modelis used to compute the air change rate based on the aspect ratioof the room, dimensions of the window orifice, surroundingbuilding height, and wind direction and velocity taken from

Figure 5 Heat exchange with the air in a room. Arrowsindicate possible directions of heat flow.

Figure 6 Thermal circuit for heat exchange through asample window.

Figure 7 A sample airflow window: air enters the cavitybottom and exits at the top. Intake and exhaustvents can connect to either the room interior orthe outdoors.

Δd

BihΔd

min

k------------------ 1«=

Fok

ρcmass

----------------⎝ ⎠⎛ ⎞ Δt

max

1

Δd2--------- 0.5≤=

6 Buildings X

the weather data. Windows are opened or closed in a logicalmanner based on the outdoor and indoor temperature to helpimprove occupant comfort and reduce HVAC loads.

Assuming air is well-mixed within a zone, an energybalance on the air within a room can be written as

(3)

where

= the heat capacity of air

= the mass flow rate due to ventilation

= the mass of one roomful of air

= instantaneous room temperature

= outdoor temperature

Discretizing Equation (3) in time with an implicit formulation(using in the RHS) and solving for room temper-ature at the next timestep yields the following:

(4)

where is computed once at the start of each hour andremains constant during that time period. The values of

, , and are computed at the start of eachtimestep explicitly using as a room temperature. Theseloads are assumed constant over the duration of . For thisassumption to hold, the timestep t must be small enough suchthat the air temperature does not vary substantially from onestep to the next. A conservative 5 minute time step is currentlyemployed with good result. At each timestep Equation (4) issolved to predict the room temperature at the next time step.

If a temperature bound is exceeded, heating or coolingHVAC loads are applied to return the zone-air to the upperbound temperature when the air becomes too hot, or the lower

bound temperature when the air becomes too cool. The magni-tude of the heating/cooling load is simply as follows:

(5)

where the room temperature at then takes the value ofthe boundary (max or min) that it crossed. If natural ventila-tion is permitted, the windows are adjusted (opened or closed)to assist with heating or cooling. This will adjust the mass flowrate of air from the mechanical rate to that which arises fromenvironmental conditions. Heating and cooling loads aretallied and monthly and annual loads are reported.

Optimizer

A building design optimizer has been developed (Lehar2005). The user selects a given scenario that has been inves-tigated and indicates which of the major design parameters areallowed to vary or be held fixed among: window typology,glazing type and area, room dimensions, insulation type andbuilding orientation. Maximum and minimum thresholds forsome parameters can be specified, such as window area androom dimensions. The program then searches though a largearray of previous solutions to determine the parameter set thatyields a minimum total yearly energy. A branching fuzzy-logic classifier has been implemented to interpolate within themulti-parameter space of known solutions. This is thencombined with a simulated annealing technique to locate theoptimization region. In keeping with the goal of the programfor a rapid response, the optimizer is designed to run withinone to two minutes. The object is to aid the designer bysuggesting designs that approach best energy efficiency. With-out this module the designer might be exploring a series ofdesigns that are all relatively inefficient.

DOCUMENTATION AND VALIDATION

Care has been taken to validate the program’s elementsagainst industry-accepted standards, accredited simulationsoftware, and analytic models. Detailed comparisons anddocumentation are published on the program’s website. Vali-dation results are presented for steady-state and transientwindow solvers, the thermal mass model, and a full-scale cali-brated software comparison.

Window Solver Validation

Steady-state heat flow calculations were used to validatethe window solver program. Center of glass U-Values andSolar Heat Gain Coefficients (SGHC) for normal-incidencewere computed for a variety of window configurations. TheLawrence Berkeley National Labs (LBL) softwareWINDOW5 was used for making comparisons. Optical andthermal properties were matched in both programs for fourtypes of glass: clear, blue, low-emissivity (Low-e1) and verylow-emissivity (Low-e2). Cavity depth of multi-layered glaz-ings was set at 12.7 mm. Interior and exterior convection coef-ficients were set at constant values of 4 and 26 W/m2-K,

mcair

∂T∂t------ Q

windowQwall

Qint

Qtm

m· cair

Text

T–( )+ + + +=

cair

m·

m

T

Text

T Tt Δt+

=

Tt Δt+

= mc

airTt

Δt Qwindow

Qwall

Qint

Qtm

Text

m· cair

( )+ + + +[ ]+

mcair

Δt m· cair

( )+------------------------------------------------------------------------------------------------------------------------------------------------

Qint

Qwindow

Qwall

Qtm

Tt

t

QHVAC

mcair

Tbound

Tt Δt+

–( )=

t Δt+

Figure 8 Thermal mass floor schematic.

Buildings X 7

according to the specifications of the NFRC-100 and ISO-15099 standards. Radiation coefficients were computeddynamically by both programs.

Results of the comparisons are shown in Figure 9. Mostcases agree to better than one percent. The few cases whichdiffer by 5 to 10% are multi-layered glazings with at least onelow-emissivity pane. Because radiation is reduced in thesecases, the fill gas thermal resistance is a dominant factor in theoverall heat transfer through the window. The error is likelydue to the simplifying assumption that pure conduction occursin the fill gas. In reality small amounts of convection can takeplace within the gap, assisting heat transfer. Such errors areacceptable as the intent of the program is simply to showdifferences between general types of window configurations.

Thermal Mass Validation

We have compared our thermal mass model with theanalytical solution for a seimi-infinite solid. Figure 10 showsresults for a 0.20m-thick slab of concrete with an adiabaticbottom surface. From top to bottom, the curves indicate thetemperature of the surface-node, the central-node, and thedeepest-(semi-adiabatic)-node of the thermal mass as a func-tion of time. Figure 10a shows results for a constant heat fluxof 400 W/m2 incident on the slab surface with no convection.Figure 10b illustrates the convection-only case, where the slabis exposed to a constant environmental T=320K with a convec-tion coefficient of 8.0 W/m2-K. Both cases start with a uniforminitial slab temperature of 300K and the simulation runs for aperiod of one hour. By computing an average final temperaturethrough the slab depth, the net-energy gain is found for eachcase. Agreement is better than 0.1% for the constant heat fluxcase and 2.1% for the convection case.

Daylighting Verification

Close agreement between the MIT Design Advisor andthe daylighting software Radiance has been demonstrated. Acomparison of daylight distribution over each of the roomsurfaces can be found in the reference work (Lehar 2004).

Energy Plus Comparison

A series of calibrated comparisons have been madeagainst the Energy Plus software with good agreement. Allparameters have been matched where possible. The climatedata for each simulation is that of Boston, MA taken from theMETEONORM library and used in both programs. As theMIT software has yet to implement a humidity model, allhumidity has been completely removed from the weather datafiles for the comparisons. The cases are each built from anidentical base simulation, with one or more parameters varied,and are described in Table 2.

Case 1 represents an internal load inside an adiabatic box,and the required sensible cooling load has been computed tomaintain a maximum zone temperature of 26ºC. The coolingload in Case 1 should be – and is for both models – exactlyequal to the internal loads. All other cases (2-7) have been run

to compute heating loads only, based on a minimum zonetemperature of 20ºC. Room temperatures were allowed to floatfreely above 20ºC. The high and low thermal mass cases corre-spond to floor thicknesses of 0.20 and 0.02 m, respectively.

Results of the comparison are shown in Figures 11-12 andagreement is reasonably good in most cases. The greatestdifferences occur in cases with large glazing areas and withhigh thermal mass. Differences associated with glazing areaappear to stem from differences in the U-Values. By applyingsimilar conditions to windows in Energy Plus, U-Values werefound to be at times 10% higher predicted by the MIT DesignAdvisor. Such variation is likely due to the way in whichconvection is modeled inside window cavities. Differencesassociated with thermal mass size are likely due to the simu-lation techniques employed. As an example, due to stabilityconstraints in the Energy Plus methodology, the minimumtimestep allowed is 1/6 hour.

CONCLUSION

Simulation tools, when used in early-stage design, canhelp produce buildings with improved energy efficiency. Inpractice, simulation is used only after building designs arecompleted and when it is too late to implement design revi-sions. The large time investment associated with preparingsimulations and interpreting their results prohibits their use atthe early stage. Complex user input options further prohibitsnon-technical users, including architects and building design-ers, from even attempting to analyze their designs withoutspecial training.

The MIT Design Advisor has been presented as onesimplified tool for quickly exploring the energy requirementsof competing design concepts. A restricted input space hasbeen described to minimize setup time. A rapid thermal modelhas been described for computing and graphically displayingmonthly and annual energy loads. Comparisons with industry-accepted software tools and analytic models have been madewith good result.

Preliminary trials with students and architects haveshown encouraging results. Simplified approaches to simula-tion can be useful for both educational and practical purposes.

ACKNOWLEDGMENTS

Project support has been kindly provided by theCambridge-MIT Institute and by the Permasteelisa Group.Special thanks to Maaike Berckmoes and Henk DeBleecker ofPermasteelisa for their helpful suggestions and guidance. Wekindly recognize the contribution of MIT graduate student BetsyRicker in helping to prepare the Energy Plus comparisons.

REFERENCES

ASHRAE. 2004. ASHRAE 62.1-2004. 2004. Ventilation forAcceptable Indoor Air Quality. American Society ofHeating, Refrigerating, and Air-Conditioning Engi-neers, Inc.

8 Buildings X

Figure 9 Steady-state comparisons of (a) U-value and (b) SHGC.

(a) (b)

(a) (b)

Figure 10 Thermal mass comparison, temperature distribution in a concrete slab: design advisor (dots) versus closed-form,semi-infinite slab solution (solid curve) for (a) constant heat flux and (b) convection.

Figure 11 Annual thermal load comparisons. Figure 12 A monthly comparison: Case 7 withlow mass.

Buildings X 9

ASHRAE. 2001. ASHRAE 90.1-2001. 2001. Energy Stan-dard for Buildings Except Low-Rise Residential Build-ings. American Society of Heating, Refrigerating, andAir-Conditioning Engineers, Inc.

ASHRAE. 2005. 2005 ASHRAE Handbook – Fundamen-tals, 31.16. Atlanta: American Society of Heating,Refrigerating, and Air-Conditioning Engineers, Inc.

DOE-2. 2006. Lawrence Berkeley National Laboratory. Uni-versity of California, Berkeley.

EnergyPlus. 2006. Energy Plus Software v.1.4. U.S. Depart-ment of Energy. http://www.eere.energy.gov/buildings/energyplus.

DOE-2. 2007. eQUEST the Quick Energy Simulation Tool.http://doe2.com/equest.

Glicksman, L.R., et. al. 2006. MIT Design Advisor. http://designadvisor.mit.edu. Massachusetts Institute of Tech-nology.

ISO15099:2003. ANSI Standard. Thermal performance ofwindows, doors and shading devices – Detailed Calcula-tions.

LBL. 2007. WINDOW 5.2 software. Lawrence BerkeleyNational Laboratory.

Lehar, M.A., Glicksman, L.R. 2003. A Simulation Tool forPredicting the Energy Implications of AdvancedFacades, Chapter 3, Research in Building Physics (ed. J.Carmeliet, H. Hens, and G. Vermeir), A.A. Balkema,Tokyo, pp. 513-518.

Lehar, M.A., Glicksman, L.R. 2004. Rapid Algorithm forModeling Daylight Distributions in Office Buildings.Accepted for publication in Building and Environment.

Lehar, M.A. 2006. A branching fuzzy-logic classifier forbuilding optimization. PhD Thesis Mechanical Engi-neering Department, MIT.

NFRC. 2002. NFRC 100:2001 Procedure for DeterminingFenestration Product U-Factors, 2nd ed. National Fenes-tration Rating Council Inc.

MeteoNorm Global Meteorological Database for SolarEnergy and Applied Climatology. 2000. CD-ROM byMeteoTest, Bern.

Radiance Synthetic Imaging System. 2002. Lawrence Berke-ley National Laboratory. University of California, Ber-keley.

Siegel, R., and Howell, J.R. 2002. Thermal Radiation HeatTransfer, 4th ed., p.778. New York: Taylor & Francis.

U.K Building Code. 2000. The Building Act 1984 TheBuilding Regulations 2000, Part L. Office of the DeputyPrime Minister.

U.S. Green Buildings Council. 2005. LEED-NC GreenBuilding Rating System for New Construction & MajorRenovations v.2.2. https://www.usgbc.org/Show-File.aspx?DocumentID=1095.

WinDat. 2006. Window Information System. WIS Software.http://www.windat.org/.

Table 2. Energy Plus Comparisons

Case Number Description

Base Case(Features in Common Unless

Otherwise Noted)

Location: Boston, MA, no humidityPrimary façade orientation: ERoom dimensions: 5 × 5 × 3 m (width, depth, height)Mechanically ventilated buildingAdiabatic walls (×4)Adiabatic ceiling (×1)Thermal mass floor (×1) with adiabatic lower surfaceZero % windowsInternal load: zeroVentilation rate: zero

Case 1 Base case + 6 W/m2 internal load from 7a.m. to 8p.m.

Case 2 Case 1 + 1.8 ach from 7a.m. to 8p.m.

Case 3 Case 1 + east facing wall has R-value of 4.6 m2·K/W

Case 4 Case 3 + 1.8 ach from 7a.m. to 8p.m.

Case 5Case 3 + triple-glazed clear-pane window on east wall comprising 84% of wall area; remainder of wall is adiabatic

Case 6 Case 5 + 3.6 ach from 7a.m. to 8p.m.

Case 7 Case 5 + internal loads and ventilation ON 24 hours per day

10 Buildings X