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An interface system for computerized energy analyses for building designers
ELSEVIER
Energy and Buildinga
27 ( 1998)
97-107
An interface system for computerized energy analyses for building designers
Abstract This paper describes research on a computer model for building energy simulation. The main goal of this effort has been to provide building designers a better design tool for energy optimiration in their building design processes. With the rapid development in computing technology. today’s expectation for computer-aided building design (CABD) systems have matured to an advanced level-to the point where designers are open to the idea of computers helping them in the thought process and not just being used to manipulate and represent geometric shapes (J. Pohl et al., 1992. A Computer-Based Design Environment-Implemented and Planned Extensions of the ICADS Model. Design Institute Research Keport, CADKU-06-92, California Polytechnic State University. San Luis Obixpo. CA. 1; [ J.A. Clarke, A.D. Irving, 19X8. Building Energy Simulation: An Introduction. Energy and Buildings, IO ( 198X), Elsevier. pp. 157-I%].]. By its very nature. design is a multidisciplinary and creative activity which encompasses all the relevant area\ to be considered during the design process. Thus, the CABD system of today is expected to be a general purpose integrated system. rather than a domain-dependent isolated graphicy system [J Pohl, L. Myers. A. Chapman, .I. Cotton, 1989. ICADS: Working Model Version I, Design Institute Research Report. CADRU-03.89. CaliforniaPolytechnic State University. San Luis Obispo, CA.]. This research effort embodies a prototype interface system for a building energy simulation model ( ENEKife). This interface system is not a simple layer between user and simulation model. but rather, a general interface strategy to control simulation models and relevant databases to be integrated into CABD. The system includes a user interface module. system interface module. general database handling module, automated input processor. building matrix system, and a result analysis and recovery system. ‘cc’,19% Publiahed b> Elsevicr Science S.A. Kuv\l~~~nlv:Energy simulation: Building design: Integrated CAD system:Interface system __--
1. Introduction
Currently, a number of dependable and well-known energy simulation models exist for the energy assessment of buildings. Most of these building energy simulation models are modified versions of mainframe analytical models originally developed for research purposes [ I .2 1. Such research-oriented deiailed simulation models require very rigorous input information about the building from users regardless of their application domain [3,4]. Moreover, the unavailability of a
detailed description of a building at the preliminary design stage prevents building designersf’rom testmg their design variables at an early design phase/ 51. Subsequentlydcveloped simplified algorithm tools, in spiteof their convenience ___. * Corresponding author. 0378m7788/48/$19.00 PI/ So37t:--‘7X8(
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in use. do not provide enough predictive validity. Furthermore. the analytical interpretation of the resultsof such simulation results does not easily take place in a building designer‘s hand. Consequently. many practicing architects considerthe useof suchsimulation modelsas a tedioustask that frequently requires the assistanceof a building energy specialistduring the design process.Thus, thesesimulation modelshave beenregardedasdesignanalysistools for energy specialists,not design synthesistools for architects [ 6 1, If the analysis in the energy performance of a certain building IS inevitably required for the hypothesized building design. then the energy specialistis usually employed to analyze and assessthe performance of the building in terms of energy usagenear the end of the design procedure. With suchdifficulties, tinal building designsdo not usually Ireflectadvanced energy conceptsthat are implementedin the detailed simulation models.and they have produceda large numberof less rewrved
energy-efficient buildings [ 71 due to a lack of detailed analysis with an energy simulation model. There has been an approach that utilizes knowledge bases to represent several problem domains in building design and a geometry interpreter to communicate in the system with graphics. In a prototype system developed by Pohl et al. [ 8 ] , the Geometry Interpreter extracts the graphical information as architectural geometric objects out of the point/line schema of the CAD drawing system. Then. evaluation tools called IDT’s (Intelligent Design Tools) evaluate theevolving design solution on a continuous basis without user interaction. Lack ofrigorous analysis by building performance aimulation algorithms will make Pohl’s prototype [9] more suitable for preliminary design of the buildings. However. one of the most significant accomplishments of this system is that it identifies required geometric information locally for each IDT by means of incorporation of the Geometry Interpreter into a blackboard tnodule of the system utilizing cxisting drawing package in spite ofoverwhelmingly largeamount of information necessary to identify the state of the de,@. Recently. from a detailed analysis of the design process, the Building Design Advisor ( BDA) formulates the iterative and interactive activities that contribute towards the synthesis of design criteria, the generation of potential solutions. and their evaluation [2,4.10]. According lo Papamichael et al. (41, in addition to the graphic user interface (GUI). this building design support environment allows to navigate through the object-oriented model of the building. and perform computations utilizing several analysis tools linked to BDA. There are similar approaches interfacing bu;ilding designers with visualization capability for the convenient building performance analysis [ 1 I. I2 1. This paper addresses energy performance analysis integrated into the CABD system to increase design performance of the building at an early design phase. It is highly important at the stage that the expertise of the energy specialist be in hand throughout the simulation process. A newly developed interface system (ENERife), with an hour-by-hour building energy simulation model ( ENERCALC ), enables building designers to test and retrofit their hypothesized building design from the early stage of the design process utilizing predefined I5 building types. 194 weather sites, and code compliance analyses.
the early design process. During its operation. ENERCALC performs: hourly weather data generation; envelope materials and glazing cataloging; userprofile definitions foroccupancy. ventilation. hot water use, lighting, and thermostat settings; zone processing; building geometry processing; load calculations, system simulations (from a database of prototypical systems). and energy summations: life-cycle cost projections 1131. In ENERCALC. weather data is generated on an hourly basis, producing the variables of dry-bulb temperature. dewpoint temperature. wind speed, sun angles, cloud cover fraction, direct insolation, and diffuse insolation. Luminous values are derived from the thermal radiation by use of a luminous efficacy algorithm. This model permits the user to specify any of 194 cities as well as the period of desired simulation each month-3 1 days, 2 I days, 14 days, or 7 days. Concepts surrounding the weather simulation model and data compression techniques are described in separate publications [ 14- 161. There are several major features that make ENERCALC most appropriate for the adoption into the prototype interface system. ENERife. In ENERCALC, Envelope Materials Cataloging permits any wall, roof, or glazing assembly to be specified and entered in a simple, numbered catalo g. Each rnaterial is defined by its name, thickness, thermal conductivity, thermal capacitance, density, and absorptivity of the outside surface to solar radiation. Glazing assemblies require input of overall thermal conductance, thermal radiation transrnissivity, emissivity. and luminous transmissivity. Load Calculations. System Simulations. and Energy Summations are performed simultaneously each hour, beginning at 12:OO a.m. on January I and ending at 1 1:OO p.m. on December 3 1, The loads are based on the exterior weather conditions and i.he interior temperature dead bands and interior heat gains. ‘The interior zone temperatures are permitted to float within the dead bands and are not assumed to cause a load until the upper or lower dead band limit is encountered. At that point the conditioning equipment is turned on in response to the
2. ENERCALC 3. The interface system (ENERife) ENERCALC is an hour-by-hour energy simulation model driven by a statistical weather data generator. This program provides reliable life-cycle energy use projections while the building is still in its formulative design phases [ 13 ]. Therefore. the major considerations in its development were that the computer run-time be reasonably short, input time be modest. and the algorithms be as rigorous as possible. This program supports the idea that the detailed methodology is a viable alternative to simplified methods even during
To integrate design decision support systems into the computer-aided design, so that each individual design decision communicates its design knowledge and information with the other systems. it needs to establish standardized information and data conveying schema and demonstrate it for a selected application domain. The interface systern structure provides an integrated environment in which several application module can be plugged. With such structure. any other design
General Database Handling Module Automated Input Processor
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Fig. 2. Brief
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decision support systems can be integrated into the system within the same paradigm illustrated in this prototype with minor adjustment and modifications. The interface system controls the overall flow of information. The system is composed of user interface module, system interface module, general database handling module, automated input processor, building matrix generator. and result analysis and recovery system (Fig. 1). Building description information generated by the geometry represen-
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tation module is then interpreted modified by an interpretation module and converted to the interface system. The interface system communicates with each individual application program module to produce simulation results. Building energy simulation module is connected to the interface system by way of the automated input processor. The interface system accommodates nine substructure modules (project identitication, weather information. building economy data, opaque envelope, glazing. building usage profiles, building descrip-
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.
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tion, system description, and zone information) to convey the overall building description to the input processor. Basically, interface system can generate a building description which is the main source of input information to ENERCAL<:, only by specifying building type, size of the building and weather location. At the initial stage of design, as the concept is illustrated in the system, a building designer goes through the simple preliminary development steps of; ( I ) project identification. (2) building type selection, ( 3) weather location selection. and (4) matrix building description (Fig. 2). The user interface has been designed to utilize a menu screen and a scroll bar to navigate through menu items on the screen (Fig. 3). While the building designer is working with the u$er interface. the system interface interacts with databases and file handling modules in the background.
4. Building type-dependent
more suitable values are found, a user can modify these values in the matrix table for the project. The Building Usage Profile Database consists of a set of eight daily usage and utilities profiles for each building type. Each profile is constructed with 24 values representing each hour in a day (Fig. 4), and a set is retrieved by the system when a building type is specified. These profiles are then catalogued by ENERCALC during its run time. The profile
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Currently, building type-dependent variables are formulated in a file consisting of I.5 sets of 35 different variables (Table I ). With this information, a primitive building configuration can be constructed without much effort at the programming stage of the building design. There are two other ways required to derive a precise energy evaluation. First, project building description can be modified manually as the design progresses, which is also provided in the system. Simulation results will be continuously compared with parametric run results established by a normalized configuration value (called Target Energy in the system ). Possible energy consumption patterns will be strictly dependent on number of building types and number of weather locations available in the system ( currently Z!c)IO palterns). Second, by modifying the building type-dependent variables in the matrix table, changes will take place in both project energy loads and parametric run results. This structural modification will cause alteration in the relative values of target energy loads. Since this matrix is constructed with the normalized values predetermined by human domain experts, some values in the configuration matrix tnight be controversial in a specific project. Whenever controversy occurs and
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5. Building geometry description At present. a simple matrix system is being utilized for the building geometry description in the system. This is done so it can be easily utilized on most personal computers with conventional color graphics drivers. The building floor area can be representedsimply by highlighting the appropriate grid cell in the matrix system. In the system prototype. a building matrix interpreter has the functions required by a feature extraction module asdescribedin the paradigm. The building matrix system generatesbuilding geometry in graphicalfl.)rm. Eachcell in the 44 by 3 I grid matrix system can be highlighted to representbuilding floor (Fig. 5). The grid scaleand color of the grid cells are specified according to size of the building and numbersof thermal zones to be
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database tile is also easily accessible by the user when modification is required as stipulated for building type-dependent variables. In case the system involves more detailed building types. the system can be modified to accommodate such building types by simple addition of a set of profiles for each additional building type both by the user and the developer.
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representedrespectively. The building can be al,sorotated by selecting a different bearing angle in building orientation menu. The building matrix system is mainly representative for building plans;however, floor height andnumberof floors are alsospecifiedto depict vertical featuresof the building.
6, Result analysis and recovery system To achieveeffective result analysisandrecovery, two main approacheshave been implemented.First. most simulation results are representedin several graphical formats. Visualizing complicated technical data in a graphical format is a viable steptoward effective interpretation of number-crunching energy simulation results in a building design process. Average hourly heating loads,average hourly cooling loads, and combinedaveragehourly loadsare representedin a three dimensionalcontour format utilizing three setsof 288 data points for a year acquired from simulation results. Several bar graphs and a line graph are also utilized to represent heating and cooling load distributions. building energy performance, and monthly heating and cooling loads.
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Second, the accuracy of the results from an energy simulation is dependent upon the method used and the experience of the individual performing the analysis. By representing a normalized target energy consumption for every case study for the project. any extreme or extraordinary figures in energy usage pattern at the project building can be easily traced and identified, and so major errors can be avoided. The comparison criteria include the annual building component heating and cooling loads for both total and peak, the monthly heating and cooling loads. and the building energy performance (Figs. 8-1 I ), Simulation results for the project building are compared with each corresponding energy consumption pattern of the target building. which is produced using normalized building configurations. Normalization of building configuration for different building types has been accomplished utilizing various data sources [ 17-7-O] and screened by domain experts.
If average hourly heating data are required from the simulation, the simulation model will generate an extremely large amount of data ( 8760 h/yr). This volume of data can cause not onty significant storage and processing time for the computing system but also the parameters of interest may be scattered through several reports and buried by many pages of output. This 3-D annual protile provides the user with a substitute method to check overall heating loads, potential problems with user input, and inappropriate modeliing conditions.
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-
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To obtain 3-D annual profiles from average hourly heating loads, hourly loads calculated from the simulation are divided by the number of days of the month which is dependent to simulation length. and then summed up in the monthly loop routine of ENER.CALC. According to the magnitude of the data values and the physical CRT resolution, scaled proportions were utilized instead of fixed coordinates. With the flexibility of the interface system, an upgraded graphic representation tool can replace it at any time in the future.
A 3-D contour profile for the average hourly cooling loads is represented in the same manner as a heating loads contour profile. To illustrate entirely positive loads (for cooling), the base plane is shifted downward proportional to the magnitude of peak point. Since both heating and cooling loads are dependent on the HVAC system, partial peaks can be observed easily at the points where changes for thermostat setting and daily occupancy schedule are specified using usage profiles in input mode. The shape of this contour profile usually forms a convex shape upward at the center of the net (Fig. 6). 6.3. Combined
hourly heating und cooling lauds
This 3-D contour profile is produced with overlapping average heating loads and average cooling loads together (Fig. 7). In fact. valuesrepresentedwith this profile are only
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correct when either heating or cooling load values zero since there are neutralizing effects in some points where positive load (cooling load) and negative load (heating load) OCCUI in the same hour. However. this combined load contour clearly illustrates the annual thermal characteristics 01’ the project building in spite of the neutralizing effects. With this approach, any significance in energy usage pattern of thr: project building can be easily noticed and traced.
7. Comparison
analysis with target energy
The normalized building configumtion utilizes building description parameters required by the energy simulation model (ENERCALC). Files containing wall assembly. win.dow assembly. and usage and utility profile are cataloged b), the system interface according to building type selected. At present. there are IS building types specified in the system (Fig. 3). Once a building type is selected, the system interf&e reads in 35 configuration values and catalog nmnber?. (Table 1 ) to access the databases. The system interface then plugs all the individual variables into the temporary input modules and utilizes catalog numbers for envelope material assembly and building usage and temperature proliles. Besides the individual variables. this catalog file contains mainly HVAC system dependent figures and building economic data. Whenever system dependent tigures and economic data get complicated and extensive, it is better to construct separate databases, and access them just using catalog numbers. The building description configured by the system interface for a target energy consumption. has the same floor area and same number of floors. but the shape of the building is different. The normalized configuration maintains 5 by 3 ratio rectangular shape. This proportion may be somewhat arbitrary in some cases. however it represents a target energy consumption good enough to be used based on repeated experiments. The other default figures for a specitic building type are adopted from various reference sources [ 17- 19.2 I 1. To determine whether the energy consumption of a pro,ject building is appropriate, comparison criteria should not be overlooked. Energy consumption criteria prepared using the normalized target building configuration present a valuable energy performance evaluation tool at the early stage of building design. The Target Energy provides not only whole-building energy performance but also individual building component energy usage patterns. Even though the eventual goal would be the whole-building energy performance, a building designer can recognize any specific building attributes to be improved and accomplish substantial energy coliservation of the design suggested by the system. There have been some debates about the advantages and disadvantages between the performance-based building energy standards and the prescriptive building energy standards [ I8,22,23]. While the performance-based building energy standards allow building designers flexibility of
design, the prescriptive approach also provides them with valuable guidance toward energy efficiency at the early stage of design [ 24 J The Target Energy presented by the system provides not only whole-building energy performance (BEPS) (Fig. I I ), but also individual building components comparison criteria for the convenience of retrofits ( Figs. 810) [25,261. Comparison analysis of peak heating and cooling loads resulting from design retrofits can potentially contribute for a proper HVAC system selection to shave peak loads and a benefit of reduced sizing of the heating and air conditioning systems (Fig. 8). In the system. annual heating and cooling loads are presented for nine different building energy conponents: ( I ) roof, (2) external walls, ( 3) window transmission. (4) window direct solar gain, (5) lighting and equipment gain, (6) people’s sensible load, (7) people’s latent load. (8) inliltration and ventilation load, and (9) building mass effect. Each component is compared with the target energy consumption for identilication of obvious excessive loads in the project building (Fig. 9). If the Building Energy Perl’ormance represents significant difference between the target energy and the project building. the user identifies one or more building components suggested on this component heating and cooling loads graph to analyze and revise. Monthly heating and cooling loads are presented in a line graph with foulr different curves: Project Cooling. Target Cooling, Project Heating. and Target Heating (Fig. 10). The magnitude is scaled automatically according to the maximum load acquired from the simulation results. Building Energy Performance has the unit, million W/h per square meter of building floor area per year. The eventual goal for the energy conservation in the project building is set at this building energy performance value ( Fig. I I ). For the convenience of building designers. both site line performance and source line performance an: represented from the simulation results. In Building Energy Performance Analysis, the source energy of the project building represents 3.7% higher ( 1 167 kW h/m’ yr vs. 851 kW h/m’ yr) than the target energy (Fig. 1 I ). And, the Monthly Heating and Cooling Load (Fig. IO) reveals the cause, the significantly high project cooling loads during April through October compared to the target energy. From the Annual Cooling Load Distribution (Fig. 9). it is evident that the main differences in loads are attributed to the wall conduction, window gains. and lighting/ equipment gains. Previous research [ 251 successfully approached the target energy almost identically by applying: ( 1 ) 2 mZ K/W (R-I 1) insulation to the external walls. ( 2) selective controls of lighting power density in perimeter zones. and (3) insulated glazing and shading device to the windows. 8. Conclusions Realization of computer-supported architectural design will come after the integration of the computing system into
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the design process. A broader sense of CABD system integration is necessary not only for incorporating computing systems’ capabilities to assist designers in the decision making process, but also to automate a large extent of the design process and to transfer design information effectively from one stage to the next, so that the designer can be exposed to as much information during the design process. Information extraction from geometric models to use in mathematical models for the computing system, or vice versa, has been problematic due to the domain discrepancy between those virtual realities of the same subject. Utilizing an energy simulation model as an important design decision support system in architectural design process, a prototype interface system involving an information extraction schema is developed and discussed. Furthermore, it is emphasized that several databases need to be shared as common denominators to make information abstraction and extraction conventions more interactive. The concept of the normalized building configuration is one of the major contributions of this research both for dynamically describing the building at the preliminary design stage and for producing target energy values for comparison at the results stage. Utilizing user-definable control methods for domain knowledge. design information databases, and modularized application modules, the input/output strategies for the interface system (ENERife) is constructed to effectively interact with the CABD system. In addition, the developed interface system suggests aprototype to clarify and elucidate underlying principles for the
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procedural structure of the CABD integration paradigm. Though the interface system developed can provide immediate assistance to the designer in respect to finding an effective energy performance of the design, the eventual goal intended for the further integration has yet to be achieved. One of the major findings that have been realized through this research is that the existence of standardized schemata for information transfer is essential to have an integrated CABD system. When specific functions that were previously merged into the building energy simulation model and the other programs are being separated and modularized, the standardization of information extraction schema can only make this modular system design more meaningful and viable. However, as witnessed in the industrial arena, certain level of consolidation effort and, more importantly, the popularity of such schemata have decisive roles in the future standardization. Secondly, in the integration of building energy simulation, utilization of building types sometimes creates loopholes. Buildings are usually designed and constructed with many combinations of space functions. Even though averaged space allocation is selected in each building type in the system, ‘average’ is frequently not accurate enough to be descriptive of the true building functions. Thus, building types need to be refined more (practically) to accommodate real world situations. Furthermore, number-crunching simulation results require the adaptation of more effective scientific visualization in the result analysis and recovery section. Recently, rapid devel-
opments in video imaging systems enables hundreds of pages simulation results to be reviewed in several screenfuls of visual images. Since the interface system is designed to facilitate such individual modules, a better performing visual system can be plugged and replace the current graphic modules when it becomes available.
1 131 L.O.
13 1 J.A. Clarke, Building performance the prN>fvssion. Proceedings
of
delivering Simulation
the power to ‘89, The
International Building Performance Simulation Association. Vancouver. Canada. June 1989. pp. 30% 3 I I / 4 ] K. Papamlchael et al., The building design advisor. Proceedings 01’ the ACADlA 1996 Conference, The Association for Computer Aided Dc\ign in Architecture, Tucson. AZ. Ott 19Y6. pp. 8.5-97. I 5 j B. Jog. El~aluation of designs for energy performance using an expert system Proceedings of the 3rd International Symposium on CAD, State% Um\, of New York. Buffalo. NY, March 23-25, 1990. jh] K. Schuster. Architectural Record 172 ( 12) ( 198-l) 31-39. 171 M.R Bramblcy. M.L. Bailey. The C.S. Department of Energy’s advanced energy design and operation technologies project. Proceedmgs of Building Systems Automation and Integration Symposil~m. Univ. of Wisconsin, Madison, WI, June, 199 I. IX I J. Pohl et ‘11.. A Computer-Based and Planned Extensions of Krwar
the
Design Environment-Implemented ICADS Model, DeGgn Institute Calitr~rnio Polytechnic State Univ.
[ 9 I J. Pohl, L. Myers, A. Chapman, J. Cotton. ICADS: Working Model VeraIon I. Design Institute Research Report, CADRll-03.89. Cnlll’orni;l Polytechnic State Univ. San 1 uis Ohispo. CA, 1989. 1 IO] K.M. Papamlchael, S.E. Selkowitr. A computer-hased hulldlng design suppon environment. Proceedmgh of Building Systems Automation and lntrgratwn. Univ. of Wisconsin, Madison. WI, 19Y 1. I I I I PNL. Bullding Systems Update--No\. 1994. Pacihc Northue\t Lahoratol y ( PNL). Nov. 1994. I I2 I J.J. Hirxh et al., Design of PowerDOE, a Windowsr%ased visually ownted analysis tool, Proceedings 01‘ Building Simulation ‘9.5, lntclnational Building Performance Simulation Awxiation. Madl%on, WI.
199.5
ENERCALC:
a weather
bulb temperatures for air conditioning Kentucky Workshop on Computer Design, Lexington, KY. Apr. 1970. [ I.51 L.O. Degelman, A statistically-hascd
and
building
energy
1 Ih]
L.O. Degelman, residential and
of in dry
purposes, Proceedings of the Applications IO Environmental pp. 213-223. hourly weather data generator
for drrving energy simulation and buildings. Proceedings of Building Building Performance Simulation Nice, France, Aug.. 1991.
References I 1 1 J.A. Clwhe, A.D. Irving. Building energ) simulation: an introduction. Energy and Buildings. ( IO) ( 198X). Elscvier. 1988, pp. 157-1.59. 121 K.M. Papamichael. S.E. Selkowitz. Modeling the building design procw and expertise, ASHRAE Trans. 1990. Vol. 96, Pt. 2. SL-90-Z 7, ASHRAE. Atlanta, GA, 1990. pp. 4X l-489.
Degelman,
simulation model using fast hour-by-hour algorithms, Proceedings the 4th National Conference on Microcomputer Applications Energy, University of Arizona, Tucson, AZ. May, I990 [ 141 L.O. Degelman. Monte Carlo simulation of solar radiation and
equipment Simulation Association.
de’;ign software 91, International Sophia-Antipolis.
B.S. Kim. Solar collector performance small commercial buildings using
for
simulation for the RENCON
microcomputer progmm, Proceedings of IYth American Solar Energy Society Annual Meeting (SOLAR 901, Austin, TX March, 1990. I I7 ] AIA. Energy in Design: Techniques I 4 level 2 workshop of the AIA energy profeGonal development program). The Amerrcan Institute of Architects, Washington. 1 I8 1 ASHRAE. ASHRAE/IES of new buildings except Sociely Atlanta, [ 191 ASHRAE, Society Atlanta. I201 j :I I
[ 112
123
of Heating, GA, 1989.
DC. I98 I. Standard 90. I - 1989. low-rise rehidentiat
Refrigerating
and
Energy efficient design buildings, American
Air-conditioning
Engineer\.
Handbook-1993 Fundamentals, Chap. of Heating. Refrigerating and Air-conditioning GA. 1993.
3s.
IES. IES Li_phting Handbook, 5th edn., Itiumin;~ting Engineering Society. Nelc York, NY. 1978. BHKRA, Commercial huilding design integrating ctimatc, comfort, and cost, Burt Hill Kosar Rittlelnann Associatei/Min Kantrowitz Associates. Van Nostrand-Reinhold, New York. NY, 1987. R.S. Briggx. M.R. Bramhley. Whole-building energy targets: a methodology Building Simulation
for future Simulation Association,
R. Wehrli. 19X6.
Environmental
performance-bared ‘91. Internatwnal Sophla-Antipolis, Design
Research.
standards. Proceedings of Building Performance Nice, Frxnce. Aug., I99 I. Wile!,
New
1I!4
CEC, Rationale for Staff Proposd on Nonresidential Standards, P400-81-O 17. California Energy Commission. CA, 1983. j :!s 1B.S. Kim. An Interface Model for Building Energy Simulatmn in an Integrated Computer-aided PhD. Dissertation, Texas A&M University. 1261
American Engineers,
B.S. Kim. simulation Simulation Association,
Design TX, 1992.
York,
NY,
Buildmg Sacramento. Performance Environment,
L.O. D egelman. An intelligent front end torbuildingenergy on microcomputer enwronment. Proceeding< of Building ‘9 I, the International Bulldtng Performance Simulation Sophia-Antipolis.
Nice.
France.
Aug..
1991.
Energy and Buildinga
27 ( 1998)
97-107
An interface system for computerized energy analyses for building designers
Abstract This paper describes research on a computer model for building energy simulation. The main goal of this effort has been to provide building designers a better design tool for energy optimiration in their building design processes. With the rapid development in computing technology. today’s expectation for computer-aided building design (CABD) systems have matured to an advanced level-to the point where designers are open to the idea of computers helping them in the thought process and not just being used to manipulate and represent geometric shapes (J. Pohl et al., 1992. A Computer-Based Design Environment-Implemented and Planned Extensions of the ICADS Model. Design Institute Research Keport, CADKU-06-92, California Polytechnic State University. San Luis Obixpo. CA. 1; [ J.A. Clarke, A.D. Irving, 19X8. Building Energy Simulation: An Introduction. Energy and Buildings, IO ( 198X), Elsevier. pp. 157-I%].]. By its very nature. design is a multidisciplinary and creative activity which encompasses all the relevant area\ to be considered during the design process. Thus, the CABD system of today is expected to be a general purpose integrated system. rather than a domain-dependent isolated graphicy system [J Pohl, L. Myers. A. Chapman, .I. Cotton, 1989. ICADS: Working Model Version I, Design Institute Research Report. CADRU-03.89. CaliforniaPolytechnic State University. San Luis Obispo, CA.]. This research effort embodies a prototype interface system for a building energy simulation model ( ENEKife). This interface system is not a simple layer between user and simulation model. but rather, a general interface strategy to control simulation models and relevant databases to be integrated into CABD. The system includes a user interface module. system interface module. general database handling module, automated input processor. building matrix system, and a result analysis and recovery system. ‘cc’,19% Publiahed b> Elsevicr Science S.A. Kuv\l~~~nlv:Energy simulation: Building design: Integrated CAD system:Interface system __--
1. Introduction
Currently, a number of dependable and well-known energy simulation models exist for the energy assessment of buildings. Most of these building energy simulation models are modified versions of mainframe analytical models originally developed for research purposes [ I .2 1. Such research-oriented deiailed simulation models require very rigorous input information about the building from users regardless of their application domain [3,4]. Moreover, the unavailability of a
detailed description of a building at the preliminary design stage prevents building designersf’rom testmg their design variables at an early design phase/ 51. Subsequentlydcveloped simplified algorithm tools, in spiteof their convenience ___. * Corresponding author. 0378m7788/48/$19.00 PI/ So37t:--‘7X8(
CC 199X 07 )00057-I
Published
h!, Elsevier
Science
S.A.
All
rights
in use. do not provide enough predictive validity. Furthermore. the analytical interpretation of the resultsof such simulation results does not easily take place in a building designer‘s hand. Consequently. many practicing architects considerthe useof suchsimulation modelsas a tedioustask that frequently requires the assistanceof a building energy specialistduring the design process.Thus, thesesimulation modelshave beenregardedasdesignanalysistools for energy specialists,not design synthesistools for architects [ 6 1, If the analysis in the energy performance of a certain building IS inevitably required for the hypothesized building design. then the energy specialistis usually employed to analyze and assessthe performance of the building in terms of energy usagenear the end of the design procedure. With suchdifficulties, tinal building designsdo not usually Ireflectadvanced energy conceptsthat are implementedin the detailed simulation models.and they have produceda large numberof less rewrved
energy-efficient buildings [ 71 due to a lack of detailed analysis with an energy simulation model. There has been an approach that utilizes knowledge bases to represent several problem domains in building design and a geometry interpreter to communicate in the system with graphics. In a prototype system developed by Pohl et al. [ 8 ] , the Geometry Interpreter extracts the graphical information as architectural geometric objects out of the point/line schema of the CAD drawing system. Then. evaluation tools called IDT’s (Intelligent Design Tools) evaluate theevolving design solution on a continuous basis without user interaction. Lack ofrigorous analysis by building performance aimulation algorithms will make Pohl’s prototype [9] more suitable for preliminary design of the buildings. However. one of the most significant accomplishments of this system is that it identifies required geometric information locally for each IDT by means of incorporation of the Geometry Interpreter into a blackboard tnodule of the system utilizing cxisting drawing package in spite ofoverwhelmingly largeamount of information necessary to identify the state of the de,@. Recently. from a detailed analysis of the design process, the Building Design Advisor ( BDA) formulates the iterative and interactive activities that contribute towards the synthesis of design criteria, the generation of potential solutions. and their evaluation [2,4.10]. According lo Papamichael et al. (41, in addition to the graphic user interface (GUI). this building design support environment allows to navigate through the object-oriented model of the building. and perform computations utilizing several analysis tools linked to BDA. There are similar approaches interfacing bu;ilding designers with visualization capability for the convenient building performance analysis [ 1 I. I2 1. This paper addresses energy performance analysis integrated into the CABD system to increase design performance of the building at an early design phase. It is highly important at the stage that the expertise of the energy specialist be in hand throughout the simulation process. A newly developed interface system (ENERife), with an hour-by-hour building energy simulation model ( ENERCALC ), enables building designers to test and retrofit their hypothesized building design from the early stage of the design process utilizing predefined I5 building types. 194 weather sites, and code compliance analyses.
the early design process. During its operation. ENERCALC performs: hourly weather data generation; envelope materials and glazing cataloging; userprofile definitions foroccupancy. ventilation. hot water use, lighting, and thermostat settings; zone processing; building geometry processing; load calculations, system simulations (from a database of prototypical systems). and energy summations: life-cycle cost projections 1131. In ENERCALC. weather data is generated on an hourly basis, producing the variables of dry-bulb temperature. dewpoint temperature. wind speed, sun angles, cloud cover fraction, direct insolation, and diffuse insolation. Luminous values are derived from the thermal radiation by use of a luminous efficacy algorithm. This model permits the user to specify any of 194 cities as well as the period of desired simulation each month-3 1 days, 2 I days, 14 days, or 7 days. Concepts surrounding the weather simulation model and data compression techniques are described in separate publications [ 14- 161. There are several major features that make ENERCALC most appropriate for the adoption into the prototype interface system. ENERife. In ENERCALC, Envelope Materials Cataloging permits any wall, roof, or glazing assembly to be specified and entered in a simple, numbered catalo g. Each rnaterial is defined by its name, thickness, thermal conductivity, thermal capacitance, density, and absorptivity of the outside surface to solar radiation. Glazing assemblies require input of overall thermal conductance, thermal radiation transrnissivity, emissivity. and luminous transmissivity. Load Calculations. System Simulations. and Energy Summations are performed simultaneously each hour, beginning at 12:OO a.m. on January I and ending at 1 1:OO p.m. on December 3 1, The loads are based on the exterior weather conditions and i.he interior temperature dead bands and interior heat gains. ‘The interior zone temperatures are permitted to float within the dead bands and are not assumed to cause a load until the upper or lower dead band limit is encountered. At that point the conditioning equipment is turned on in response to the
2. ENERCALC 3. The interface system (ENERife) ENERCALC is an hour-by-hour energy simulation model driven by a statistical weather data generator. This program provides reliable life-cycle energy use projections while the building is still in its formulative design phases [ 13 ]. Therefore. the major considerations in its development were that the computer run-time be reasonably short, input time be modest. and the algorithms be as rigorous as possible. This program supports the idea that the detailed methodology is a viable alternative to simplified methods even during
To integrate design decision support systems into the computer-aided design, so that each individual design decision communicates its design knowledge and information with the other systems. it needs to establish standardized information and data conveying schema and demonstrate it for a selected application domain. The interface systern structure provides an integrated environment in which several application module can be plugged. With such structure. any other design
General Database Handling Module Automated Input Processor
I Weather Database
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Parametric Comparison Generator
_
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-
Analysis Summary Viewer
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-
J FIN
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Fig. 2. Brief
flow
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decision support systems can be integrated into the system within the same paradigm illustrated in this prototype with minor adjustment and modifications. The interface system controls the overall flow of information. The system is composed of user interface module, system interface module, general database handling module, automated input processor, building matrix generator. and result analysis and recovery system (Fig. 1). Building description information generated by the geometry represen-
Ibr ENI3it’e
intcrtace
New
Weather
Location
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tation module is then interpreted modified by an interpretation module and converted to the interface system. The interface system communicates with each individual application program module to produce simulation results. Building energy simulation module is connected to the interface system by way of the automated input processor. The interface system accommodates nine substructure modules (project identitication, weather information. building economy data, opaque envelope, glazing. building usage profiles, building descrip-
I 00 -
Select
Building
Type:
.
Office
Elementary School Secondary School Theater/Cinema Hospital Clinic Fast Food Restaurant Full Menu Restaurant Gymaaium
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1
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type
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Table Current
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tion, system description, and zone information) to convey the overall building description to the input processor. Basically, interface system can generate a building description which is the main source of input information to ENERCAL<:, only by specifying building type, size of the building and weather location. At the initial stage of design, as the concept is illustrated in the system, a building designer goes through the simple preliminary development steps of; ( I ) project identification. (2) building type selection, ( 3) weather location selection. and (4) matrix building description (Fig. 2). The user interface has been designed to utilize a menu screen and a scroll bar to navigate through menu items on the screen (Fig. 3). While the building designer is working with the u$er interface. the system interface interacts with databases and file handling modules in the background.
4. Building type-dependent
more suitable values are found, a user can modify these values in the matrix table for the project. The Building Usage Profile Database consists of a set of eight daily usage and utilities profiles for each building type. Each profile is constructed with 24 values representing each hour in a day (Fig. 4), and a set is retrieved by the system when a building type is specified. These profiles are then catalogued by ENERCALC during its run time. The profile
type
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variables
Currently, building type-dependent variables are formulated in a file consisting of I.5 sets of 35 different variables (Table I ). With this information, a primitive building configuration can be constructed without much effort at the programming stage of the building design. There are two other ways required to derive a precise energy evaluation. First, project building description can be modified manually as the design progresses, which is also provided in the system. Simulation results will be continuously compared with parametric run results established by a normalized configuration value (called Target Energy in the system ). Possible energy consumption patterns will be strictly dependent on number of building types and number of weather locations available in the system ( currently Z!c)IO palterns). Second, by modifying the building type-dependent variables in the matrix table, changes will take place in both project energy loads and parametric run results. This structural modification will cause alteration in the relative values of target energy loads. Since this matrix is constructed with the normalized values predetermined by human domain experts, some values in the configuration matrix tnight be controversial in a specific project. Whenever controversy occurs and
Incandescent High pressure Low pressure Halogen Mere ury
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5. Building geometry description At present. a simple matrix system is being utilized for the building geometry description in the system. This is done so it can be easily utilized on most personal computers with conventional color graphics drivers. The building floor area can be representedsimply by highlighting the appropriate grid cell in the matrix system. In the system prototype. a building matrix interpreter has the functions required by a feature extraction module asdescribedin the paradigm. The building matrix system generatesbuilding geometry in graphicalfl.)rm. Eachcell in the 44 by 3 I grid matrix system can be highlighted to representbuilding floor (Fig. 5). The grid scaleand color of the grid cells are specified according to size of the building and numbersof thermal zones to be
Size: n
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database tile is also easily accessible by the user when modification is required as stipulated for building type-dependent variables. In case the system involves more detailed building types. the system can be modified to accommodate such building types by simple addition of a set of profiles for each additional building type both by the user and the developer.
3rid 2.4
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of Day
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15
17
19 21
23
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representedrespectively. The building can be al,sorotated by selecting a different bearing angle in building orientation menu. The building matrix system is mainly representative for building plans;however, floor height andnumberof floors are alsospecifiedto depict vertical featuresof the building.
6, Result analysis and recovery system To achieveeffective result analysisandrecovery, two main approacheshave been implemented.First. most simulation results are representedin several graphical formats. Visualizing complicated technical data in a graphical format is a viable steptoward effective interpretation of number-crunching energy simulation results in a building design process. Average hourly heating loads,average hourly cooling loads, and combinedaveragehourly loadsare representedin a three dimensionalcontour format utilizing three setsof 288 data points for a year acquired from simulation results. Several bar graphs and a line graph are also utilized to represent heating and cooling load distributions. building energy performance, and monthly heating and cooling loads.
"cra,,oqra",,g
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Second, the accuracy of the results from an energy simulation is dependent upon the method used and the experience of the individual performing the analysis. By representing a normalized target energy consumption for every case study for the project. any extreme or extraordinary figures in energy usage pattern at the project building can be easily traced and identified, and so major errors can be avoided. The comparison criteria include the annual building component heating and cooling loads for both total and peak, the monthly heating and cooling loads. and the building energy performance (Figs. 8-1 I ), Simulation results for the project building are compared with each corresponding energy consumption pattern of the target building. which is produced using normalized building configurations. Normalization of building configuration for different building types has been accomplished utilizing various data sources [ 17-7-O] and screened by domain experts.
If average hourly heating data are required from the simulation, the simulation model will generate an extremely large amount of data ( 8760 h/yr). This volume of data can cause not onty significant storage and processing time for the computing system but also the parameters of interest may be scattered through several reports and buried by many pages of output. This 3-D annual protile provides the user with a substitute method to check overall heating loads, potential problems with user input, and inappropriate modeliing conditions.
Coo
1
represented
inq
Load
-
Loads
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To obtain 3-D annual profiles from average hourly heating loads, hourly loads calculated from the simulation are divided by the number of days of the month which is dependent to simulation length. and then summed up in the monthly loop routine of ENER.CALC. According to the magnitude of the data values and the physical CRT resolution, scaled proportions were utilized instead of fixed coordinates. With the flexibility of the interface system, an upgraded graphic representation tool can replace it at any time in the future.
A 3-D contour profile for the average hourly cooling loads is represented in the same manner as a heating loads contour profile. To illustrate entirely positive loads (for cooling), the base plane is shifted downward proportional to the magnitude of peak point. Since both heating and cooling loads are dependent on the HVAC system, partial peaks can be observed easily at the points where changes for thermostat setting and daily occupancy schedule are specified using usage profiles in input mode. The shape of this contour profile usually forms a convex shape upward at the center of the net (Fig. 6). 6.3. Combined
hourly heating und cooling lauds
This 3-D contour profile is produced with overlapping average heating loads and average cooling loads together (Fig. 7). In fact. valuesrepresentedwith this profile are only
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correct when either heating or cooling load values zero since there are neutralizing effects in some points where positive load (cooling load) and negative load (heating load) OCCUI in the same hour. However. this combined load contour clearly illustrates the annual thermal characteristics 01’ the project building in spite of the neutralizing effects. With this approach, any significance in energy usage pattern of thr: project building can be easily noticed and traced.
7. Comparison
analysis with target energy
The normalized building configumtion utilizes building description parameters required by the energy simulation model (ENERCALC). Files containing wall assembly. win.dow assembly. and usage and utility profile are cataloged b), the system interface according to building type selected. At present. there are IS building types specified in the system (Fig. 3). Once a building type is selected, the system interf&e reads in 35 configuration values and catalog nmnber?. (Table 1 ) to access the databases. The system interface then plugs all the individual variables into the temporary input modules and utilizes catalog numbers for envelope material assembly and building usage and temperature proliles. Besides the individual variables. this catalog file contains mainly HVAC system dependent figures and building economic data. Whenever system dependent tigures and economic data get complicated and extensive, it is better to construct separate databases, and access them just using catalog numbers. The building description configured by the system interface for a target energy consumption. has the same floor area and same number of floors. but the shape of the building is different. The normalized configuration maintains 5 by 3 ratio rectangular shape. This proportion may be somewhat arbitrary in some cases. however it represents a target energy consumption good enough to be used based on repeated experiments. The other default figures for a specitic building type are adopted from various reference sources [ 17- 19.2 I 1. To determine whether the energy consumption of a pro,ject building is appropriate, comparison criteria should not be overlooked. Energy consumption criteria prepared using the normalized target building configuration present a valuable energy performance evaluation tool at the early stage of building design. The Target Energy provides not only whole-building energy performance but also individual building component energy usage patterns. Even though the eventual goal would be the whole-building energy performance, a building designer can recognize any specific building attributes to be improved and accomplish substantial energy coliservation of the design suggested by the system. There have been some debates about the advantages and disadvantages between the performance-based building energy standards and the prescriptive building energy standards [ I8,22,23]. While the performance-based building energy standards allow building designers flexibility of
design, the prescriptive approach also provides them with valuable guidance toward energy efficiency at the early stage of design [ 24 J The Target Energy presented by the system provides not only whole-building energy performance (BEPS) (Fig. I I ), but also individual building components comparison criteria for the convenience of retrofits ( Figs. 810) [25,261. Comparison analysis of peak heating and cooling loads resulting from design retrofits can potentially contribute for a proper HVAC system selection to shave peak loads and a benefit of reduced sizing of the heating and air conditioning systems (Fig. 8). In the system. annual heating and cooling loads are presented for nine different building energy conponents: ( I ) roof, (2) external walls, ( 3) window transmission. (4) window direct solar gain, (5) lighting and equipment gain, (6) people’s sensible load, (7) people’s latent load. (8) inliltration and ventilation load, and (9) building mass effect. Each component is compared with the target energy consumption for identilication of obvious excessive loads in the project building (Fig. 9). If the Building Energy Perl’ormance represents significant difference between the target energy and the project building. the user identifies one or more building components suggested on this component heating and cooling loads graph to analyze and revise. Monthly heating and cooling loads are presented in a line graph with foulr different curves: Project Cooling. Target Cooling, Project Heating. and Target Heating (Fig. 10). The magnitude is scaled automatically according to the maximum load acquired from the simulation results. Building Energy Performance has the unit, million W/h per square meter of building floor area per year. The eventual goal for the energy conservation in the project building is set at this building energy performance value ( Fig. I I ). For the convenience of building designers. both site line performance and source line performance an: represented from the simulation results. In Building Energy Performance Analysis, the source energy of the project building represents 3.7% higher ( 1 167 kW h/m’ yr vs. 851 kW h/m’ yr) than the target energy (Fig. 1 I ). And, the Monthly Heating and Cooling Load (Fig. IO) reveals the cause, the significantly high project cooling loads during April through October compared to the target energy. From the Annual Cooling Load Distribution (Fig. 9). it is evident that the main differences in loads are attributed to the wall conduction, window gains. and lighting/ equipment gains. Previous research [ 251 successfully approached the target energy almost identically by applying: ( 1 ) 2 mZ K/W (R-I 1) insulation to the external walls. ( 2) selective controls of lighting power density in perimeter zones. and (3) insulated glazing and shading device to the windows. 8. Conclusions Realization of computer-supported architectural design will come after the integration of the computing system into
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lding
Fig. I I. Building
energy
performance
the design process. A broader sense of CABD system integration is necessary not only for incorporating computing systems’ capabilities to assist designers in the decision making process, but also to automate a large extent of the design process and to transfer design information effectively from one stage to the next, so that the designer can be exposed to as much information during the design process. Information extraction from geometric models to use in mathematical models for the computing system, or vice versa, has been problematic due to the domain discrepancy between those virtual realities of the same subject. Utilizing an energy simulation model as an important design decision support system in architectural design process, a prototype interface system involving an information extraction schema is developed and discussed. Furthermore, it is emphasized that several databases need to be shared as common denominators to make information abstraction and extraction conventions more interactive. The concept of the normalized building configuration is one of the major contributions of this research both for dynamically describing the building at the preliminary design stage and for producing target energy values for comparison at the results stage. Utilizing user-definable control methods for domain knowledge. design information databases, and modularized application modules, the input/output strategies for the interface system (ENERife) is constructed to effectively interact with the CABD system. In addition, the developed interface system suggests aprototype to clarify and elucidate underlying principles for the
Energy
comparison
1037 SOUl-Ce
Project
Building
Performance
for the sample building.
procedural structure of the CABD integration paradigm. Though the interface system developed can provide immediate assistance to the designer in respect to finding an effective energy performance of the design, the eventual goal intended for the further integration has yet to be achieved. One of the major findings that have been realized through this research is that the existence of standardized schemata for information transfer is essential to have an integrated CABD system. When specific functions that were previously merged into the building energy simulation model and the other programs are being separated and modularized, the standardization of information extraction schema can only make this modular system design more meaningful and viable. However, as witnessed in the industrial arena, certain level of consolidation effort and, more importantly, the popularity of such schemata have decisive roles in the future standardization. Secondly, in the integration of building energy simulation, utilization of building types sometimes creates loopholes. Buildings are usually designed and constructed with many combinations of space functions. Even though averaged space allocation is selected in each building type in the system, ‘average’ is frequently not accurate enough to be descriptive of the true building functions. Thus, building types need to be refined more (practically) to accommodate real world situations. Furthermore, number-crunching simulation results require the adaptation of more effective scientific visualization in the result analysis and recovery section. Recently, rapid devel-
opments in video imaging systems enables hundreds of pages simulation results to be reviewed in several screenfuls of visual images. Since the interface system is designed to facilitate such individual modules, a better performing visual system can be plugged and replace the current graphic modules when it becomes available.
1 131 L.O.
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[ 9 I J. Pohl, L. Myers, A. Chapman, J. Cotton. ICADS: Working Model VeraIon I. Design Institute Research Report, CADRll-03.89. Cnlll’orni;l Polytechnic State Univ. San 1 uis Ohispo. CA, 1989. 1 IO] K.M. Papamlchael, S.E. Selkowitr. A computer-hased hulldlng design suppon environment. Proceedmgh of Building Systems Automation and lntrgratwn. Univ. of Wisconsin, Madison. WI, 19Y 1. I I I I PNL. Bullding Systems Update--No\. 1994. Pacihc Northue\t Lahoratol y ( PNL). Nov. 1994. I I2 I J.J. Hirxh et al., Design of PowerDOE, a Windowsr%ased visually ownted analysis tool, Proceedings 01‘ Building Simulation ‘9.5, lntclnational Building Performance Simulation Awxiation. Madl%on, WI.
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