a daylighting design tool based on life-cycle cost ...

A Daylighting Design Tool Based On Life-Cycle Cost Simulation Larry O. Degelman, Professor Department of Architecture Texas A&M University College Station, TX 77843-3137

Abstract Simulation tools, previously restricted to the realm of the research community, have become the mainstay of many designers. Designers dealing with complex problems which previously had only scant data and tables, now use fast microcomputers for simulating a vast array of applications on a daily basis. This paper describes a simulation model that permits designers to evaluate the potential of daylighting of buildings based on a life-cycle cost model. As window sizes are varied, trade-offs become evident as the increased cooling loads caused by larger windows are counter-balanced by electric light savings from daylighting contributions. In addition to window size, the window shading properties also become a parameter in assessing the window’s effectiveness. Lower shading coefficients mean reduced cooling loads, but they also result in limiting the amount of daylight that can enter a space. Fully functional daylight dimmers are an essential part of the scheme in order to bring about energy and cost savings. The model weighs the investment in daylight sensors and dimmers against the annual reductions of electric lighting and air-conditioning energy. A present worth analysis is performed as the metric that determines cost-effectiveness. This model has been implemented in a Windows-based software tool for designers. Much of the development effort has been targeted on making the tool friendly enough that designers are willing to use it as part of their daily routine. Introduction and Background Computer software is rapidly becoming a common design tool in building design offices. The model described here belongs to that category of software that allows for evaluation of alternative design schemes in early design phases and is part of the ENER-WIN whole-building energy analysis package (Degelman & Soebarto 1995). The program has been used by students of architecture and architectural/engineering practitioners for evaluation of alternative energy-oriented design features in new and existing buildings. Output from the program is presented in the form of bar charts and graphs, so the user can quickly “see” the relative size of energy use components and decide which areas need corrective measures. The model emphasizes the evaluation of building envelope characteristics, fenestration design and lighting. Daylighting potential is evaluated in the software by calculating the daylight illuminance levels and the response of electric light dimmers and then projects the overall annual savings of electricity for both lighting and air-conditioning systems. This paper illustrates the use of ENER-WIN as a window design tool using daylighting as the design parameter and life-cycle cost as the objective function that is to be minimized. Results are shown for five different cities within the mainland U.S. that represent a wide range of climate types. The sequence of the presentation in this paper is to describe the ENER-WIN software, the daylighting calculation method, and the life-cycle cost (LCC) model, followed by an example of applying the model as a daylighting design tool in an office building in a specific city and an illustration of how the window design conclusions might vary across several climates.

General Description of ENER-WIN The development of ENER-WIN was based on the recognition of several simple premises, the first of which is that building designers often have to make design decisions with very little information available to them (Lawson 1990). The second is that the earlier information can be made available, the better the chance of having a successful and economical design. Third, the simpler the information and the easier it is to access, the higher the likelihood that it will be utilized. In view of these, ENER-WIN was designed to have fast access to databases, short run times, and preliminary building and system default descriptors. The software also has a convenient user-oriented interface program that connects the user to the energy simulation program and the databases. The ENER-WIN software has been built from several modules — an interface module, a weather data filing and retrieving module, a sketching/design module, and an energy simulation module (Degelman and Soebarto 1995). Because it is important to provide the designer with an easy-to-use tool, the interface was designed to allow the user to quickly input and modify the data and to rapidly execute the simulation. To facilitate this objective, the interface was written in Microsoft Visual Basic, running under MS-Windows. For ease of start-up, ENER-WIN provides an array of default values, including economic parameters, occupancy statistics, internal use schedules, lighting and HVAC system types, temperature settings, and other parameters for the selected building type. The designer’s main task is to provide the building sketch, its location, its various costs and its thermal and operational characteristics (including whether daylighting is to be used). Sketching Interface. ENER-WIN uses a simple sketching program that allows the user to sketch the building’s HVAC zones and specify the geometrical parameters, such as, the number of floors in the building, the floor-to-floor heights, and the building orientation. The user can draw up to 15 zones on every floor. Zones are simply represented in plan by different colors (see Fig. 1). After the sketching process is complete, a drawing processor will analyze the geometrical conditions, and automatically add the floor and the roof areas to the building. A very important capability of this processor is that it analyzes how the walls are shaded by adjoining and outside structures.

Fig. 1. Building sketch interface screen Energy Simulation Procedures. Energy simulations are performed hourly, beginning at 1:00 a.m. on January 1 and continuing through the whole year. Heat transfer through walls and roofs are handled by a transient heat balance technique based on sol-air temperature, time lag, and decrement 2

factor. Heating and cooling load calculations are based on a slightly modified TETD/TA methodology (ASHRAE 1997). During the design process, the user will normally want to gain quick feedback of a qualitative nature. Output from ENER-WIN is available in both tabular and graphic forms. The graphic form is provided as a bar chart that shows a breakdown of end-use energy consumption by the various building components -- i.e., by space heating, water heating, space cooling, fan motors, lighting, and receptacle loads (see Fig. 2.) With a quick inspection of this bar chart, the designer is informed as to which components may be potential candidates for energy-reducing measures. A higher level of detail may be inspected later by calling up the tabular results. The tabular results will consist of the following outputs: breakdown of monthly energy loads and utility bill predictions, energy savings from utilizing daylight, peak load analyses, demand charge evaluations, 24-hour energy use profiles, and life-cycle cost of the building and its systems. By using consecutively generated bar charts, the designer can quickly observe the impact of use of daylight dimmers (see Figs. 2 and 3.)

Fig. 2. Annual breakdown of energy end-use (Houston office bldg. with no d.l. dimmers)

Fig. 3. Annual breakdown of energy end-use (Houston office bldg. with d.l. dimmers)

Description of the Daylighting Calculation Model Interior horizontal illuminances on the work plane are based on a modified Daylight Factor (DF) method. First, the daylight illuminance on the vertical glass is calculated from an efficacy equation that converts the incident thermal radiation to luminous radiation (Gillette and Treado 1985). A DF is then multiplied by the vertical illuminance to derive the illuminance on the work plane within the rooms. The DF’s were derived from empirical data that were fit to an exponential curve that diminishes the values as the distance from the outside window increases (Abdulmohsen et al 1994). The program allows the option of using the empirical DF method or the IES Lumen Method. The IES Lumen method has been modeled in equation form earlier work by the author (Degelman et al 1988). Light sensors and electric light dimmers are assumed to be located within the daylit zones at intervals of about 3 meters measured from the outside wall. The illuminance detected at a three meters depth will control the lights in the first 3-meter zone of the room; the illuminance detected at six meters will control the lights in the second 3-meter zone; etc. The program assumes that fullyfunctional continuous dimmers are operating independently of each other in each of the lighting zones within the rooms. The algorithm reduces the amount of electric lighting energy by the ratio of

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the calculated daylight illuminance to the target illuminance. When the daylight illuminance exceeds the target level, the lights are simply assumed to be turned off. The workplane height is assumed to be at 0.76 meters (30 inches) off the floor. Daylight entering any windows beneath this work plane level is not included in the interior illuminance calculations, so the user must specify both the sill height and head height of windows in the program. Input of the window area and daylight transmissivity values are also necessary in order to accurately calculate the total quantity of light entering the space. If daylight transmissivity is not known for a window, the Solar Heat Gain Coefficient (SHGC) is used as a default. Description of the Life-Cycle Cost Model The life-cycle cost prediction is the final step in the program. The model uses a Present Worth (P.W.) methodology, which is defined as the total of the first costs of construction (initial investment) plus the present worth of the future annual owning and operating costs of the building. User inputs required by the program include the owner’s opportunity (discount) rate, the current fuel and energy prices, the annual fuel escalation rates, the predicted economic life of the facility, and the peak demand charges for electric power. First costs of the building are based on the unit costs of walls, windows, and roofs from an assemblies catalog. Additional first costs include the lighting system and the mechanical system. When daylighting evaluations are conducted, the extra cost of light sensors and dimmers are required to be input as part of the lighting system unit costs. Table 1 illustrates the programs tabular output results for an evaluation of daylighting in a Houston office building. The life-cycle cost savings are only on the order of 3% of the total; however, this represents over $200,000 in the case shown. One of the interesting facets of daylighting design is illustrated in this example in which the cost savings due to a smaller air conditioning system is enough to pay for the extra investment in daylighting dimmers. As a result, the first cost of the daylight design is slightly less than the base case without daylight. Another interesting facet is that the daylit buildings always require more heating than the base case buildings. This is almost always overshadowed by the much larger savings in air conditioning and lighting energies. The example in Table 1 uses a cost of $2000 per ton for air conditioning and $8.00 per sq.m. for daylighting controls in the daylit zones. Fuel prices are based on $0.065 per kWh for electric and $6 per GJ for gas. Examples of Daylighting Evaluation in Several U.S. Climates A prototype office building is presented here as an example of how daylighting design can be effectively evaluated on the basis of life-cycle costs. The building is a 6-story office building that has already been designed to meet ASHRAE/IES Standard 90.1-1989 (ASHRAE 1989), EnergyEfficiency Standard for New Buildings. It has passed the ENVSTD (envelope criteria) and LTGSTD (lighting criteria) software tests that are provided with the Standard. The example building has a 41x32 m (135x105 ft) rectangular plan. Each floor has a perimeter zone of 4.5 m (15 ft) depth, accounting for about 584 sq.m. (6290 sq.ft.) of daylit area and a core area of 731 sq.m. (7870 sq.ft.). So, around 44% of the total floor area is subject to daylighting. The gross conditioned area of the building is 7890 sq.m. (85,000 sq.ft.) The walls are metal panel with a U-factor of 0.9 W/sq.m.K (0.16 Btu/h.sq.ft.F.) The windows are single-pane reflective glass, low-e coating, and a U-factor of 5.85 W/sq.m.K (1.03 Btu/h.sq.ft.F.) The window’s overall Solar Heat Gain Coefficient (SHGC) is 0.29. 4

Table 1. Life-Cycle Cost Evaluation of Daylighting Systems in Sample Building Life-Cycle Cost Summary for New Office Building in Houston (IAP), Texas ! PLAN: 1 TYPE: Office Weather Year: 1998 Date of Run: 22 Jan 1998 Year building evaluation begins: 1998 Project economic life: 20.0 Years. Life-Cycle costs are in terms of Present Worth in 1998

Item Construction (frame and site) Lighting HVAC Walls, Roof, Floor Slab Windows ----------------------------------Total 1st Cost $/Sq.m. Present Worth of: Gas (Space & water htg.) Elec (a.c., ltg, & equip.) Maint (a.c. systems) Total P.W. of O&M ----------------------------------Total Present Worth $/Sq.m.

Total First Cost ($) w/o Daylighting w/ Daylighting 4252280 4252280 167362 195682 320561 285070 259290 259290 124430 124430 ------------------------------------------------5123924 5116752 649 648 P.W. of Future Energy Costs ($) w/o Daylighting w/ Daylighting 51566 57294 1175535 992104 193310 171908 1420411 1221386 --------------------------------------------------6544334 6338138 828 802

% change 0 +16.9 -11.1 0 0 -------------0.14

+11.1 -15.6 -11.1 -14.0 -3.2

The daylighting depth was set at 4.5 meters (15 feet) and the target illuminance at 430 lux (40 footcandles). The electric lighting system was ceiling-mounted fluorescent fixtures with a power density of 17 W/sq.m. (1.57 W/sq.ft.) The receptacle power density was 8 W/sq.m. (0.75 W/sq.ft.) According to the Dodge Reports (1996) and the National Construction Estimator (NCE 1995), the installed cost of a light sensor is about $106 each and the continuous dimmer switch is around $77 ea. Each sensor-dimmer combination was estimated to cover about an area of 23 sq.m. (244 sq.ft.) or a floor space of 4.5x4.5 meters, resulting in an incremental investment of $8 per sq.m. This was added to the installed price of the lighting system. Electricity prices were based on a U.S. national average of $0.065 per kWh, reflecting a combination of actual energy costs plus demand charges. The first LCC investigations were carried out on only one city (St. Louis). This city has a temperate climate, with a significant level of annual heating and cooling. Fig. 4 illustrates the conventional conclusions that are usually associated with increasing the amount of glass on a large office building. The glazing varied from 20 to 70 percent of the wall area and shows quite clearly that costs increase with increased window area. Four parameters are shown in the figure -- first costs for windows and HVAC, owning and operating (O&M) cost, and total life-cycle cost expressed as present worth (P.W.) The O&M costs are calculated as the present worth of future annual energy used for heating, cooling, lighting, and other equipment plus the maintenance for the HVAC systems. All costs are expressed in $/sq.m. of gross floor area. Using ENER-WIN for a second set of runs, the daylighting parameter in the input screen was changed from a “No” to a “Yes”, and the daylighting depth and target illuminance were set. The resultant LCC’s are shown in Fig. 5. The value of cycling through several window size proposals is shown in this figure. 5

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Fig. 5 LCC vs. % glass in St. Louis (w/ d.l.)

The lowest LCC occurs when the glazing percentage is at 40%. Though the figure gives the impression of only a small percent drop in LCC, the tabular output revealed that this amounted to $22,153 savings over the 20% glass and $166,578 savings over the 70% glass. Further comparisons were made to the non-daylit case, and it was discovered that the LCC savings were $178,672; and, a more detailed inspection showed that not only had the LCC decreased, but the first cost investment was a break-even situation (actually a $6900 saving). So, in effect, the daylighting system was shown to pay for itself at installation time. The explanation for this is that the air conditioning size was reduced by about 20 tons, and this saved the entire cost of the installation of lighting dimmers and sensors. This is a no-risk investment, but must be approached with the intent that the daylighting systems will be not be ignored when the building in under operation. The above example shows how the software can help evaluate window sizing on a life-cycle cost basis, but there are other parameters that can be examined quickly as well. The next example will only be described briefly, results of which are shown in Figs. 6 and 7. These illustrate the examination of the window’s shading characteristics in relation to LCC. The example varies the SHGC from 0.29 (reflective low-e) to 0.45 (heat absorbing, low-e) to 0.87 (clear glass). The installed window costs were $97/sq.m., $50/sq.m., and $36/sq.m. respectively. Results for both daylighting and no daylighting show the mid-range value, i.e. 0.45, to be the optimum LCC (Figs. 6 and 7). The tabular output in this case showed only $36,867 savings for the 0.29 glass but a $217,308 savings for the 0.45 glass when compared to the no-daylight case. 350

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Fig. 7 LCC vs. SHGC in St. Louis (w/ d.l.) 6

The interactions between window sizing, shading characteristics, and daylighting versus no daylighting controls show some interesting impacts on the life-cycle cost results. The exact solutions may be unique to a specific climate in which the design is being evaluated, so four additional climates were examined briefly to determine whether the conclusions would vary in regard to the reliance on daylighting controls. The climates were chosen to represent a wide range of weather found in the U.S. -- from hot-humid to hot-arid to cold and from sunny to cloudy. These were: Seattle (cool-cloudy), St. Louis (temperate), Minneapolis (cold-sunny), Houston (hot-humid), and Phoenix (hot-arid). The purpose of this distribution was to illustrate whether the climate factors seem to favor the use of daylighting strategies in one climate more than another. This exercise might have more instructional value than practical value, but it helps to reveal the various insights that can be gained from the software. The cities were placed in order of their LCC results, Seattle being the lowest and Phoenix being the highest -- the order reflecting the cooling load dominance on overall energy consumption. This trial was only run with one window size (40% glass) and one SHGC (0.29), but the results showed consistency throughout all cities, always revealing a lower LCC for daylighting than the cases without dimmers. The first costs were also lower in every case, except in Minneapolis where the two were about even. The HVAC savings were around 20 tons for each city. Results from these simulations reveal that on an LCC basis, daylighting controls are more beneficial for the warmer and sunnier climates. Fig. 8 shows that the daylighting savings tend to be greater for the higher intensity energy users. Generally, this seems to be the hot and sunny areas (i.e., Houston and Phoenix). Fig. 9 shows the annual hours of sunshine with the cities placed in the same order as in Fig. 8. The conclusion is what we might expect -- i.e., the cities with the greater abundance of sunshine are those that benefit the most from daylighting controls.

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Fig. 9 Ann. hours of sunshine for the 5 cities

Summary and Conclusions Use of a computer simulation model to perform life-cycle costs of daylighting control systems has been demonstrated for several design parameters and for different climates in the U.S. The model appears be a very useful design tool for examining daylighting proposals for new office buildings. The examples presented here have all assumed that the daylighting design proposals are all being made during the early design process. The down-sizing of the installed HVAC system is a result of the inclusion of daylighting-controlled dimmers. In new building designs, the dimmer controls often will be paid for in the HVAC savings -- as demonstrated in the previous examples. These 7

conclusions may not apply if a retrofit situation is being evaluated. In the retrofit case, the designer does not normally have the liberty to reduce the HVAC system size, and thus the additional costs invested in the dimmer controls are all add-on costs. The energy simulation tool shown here is but one of many such simulation programs that are available today. The usability of these tools will most likely be determined by the ease of data entry and the ease of recycling through alternative designs. The amount of investment of human effort in using such tools will probably determine, to a large extent, whether such design tools become part of everyday design in the architect’s office. The software illustrated here has been written to address the ease of access issue (through a graphic interface) as well as providing the rigor of an hour-by-hour energy analysis. Acknowledgments ENER-WIN uses the ENERCALC simulation engine, which was originally developed under a subcontract from the Woodlands Development Corporation under a DOE grant to study energy conserving methods in commercial buildings in the Woodlands Metro Center. Subsequent updates have been made possible through support from the Center of Energy and Mineral Resources (CEMR), William Wayne Caudill Research Fellowship, and the Department of Architecture, at Texas A&M University. The ENER-WIN interface and Windows adaptation were developed by Veronica Soebarto. References  Abdulmohsen, Abdullah, Lester L. Boyer and Larry O. Degelman 1994. “Evaluation of lightshelf daylighting systems for office buildings in hot-humid climates”, Proc. 9th Symposium on Improving Bldg. Systems in Hot Humid Climates, Dept. of Mechanical Engineering, Texas A&M University, College Station, TX 19-20 May, pp 23-31.  ASHRAE 1997. ASHRAE handbook of fundamentals, The American Society of Heating, Refrigerating, and Air-conditioning Engineers, Inc. Atlanta, Chap 28, pp 28.56-28.65.  ASHRAE 1989. ASHRAE/IES 90.1-1989 Energy efficiency design of new buildings except low-rise residential buildings, The American Society of Heating, Refrigerating, and Airconditioning Engineers, Inc. Atlanta.  Degelman, Larry O. and Veronica I. Soebarto 1995. “Software description for ENER-WIN: A visual interface model for hourly energy simulation in buildings”, Proc. Building Simulation ‘95, International Building Performance Simulation Association (IBPSA) Conference held in Madison, Wisconsin, 14-16 August 1995, pp 692-696  Degelman, L.O., J.F. Molinelli and K.S. Kim 1988. "Integrated daylighting, heating and cooling model for atriums", ASHRAE Trans., Vol. 94, Pt. 1, January.  Dodge 1996. Dodge Reports for Building Costs 1996. F.W. Dodge Market Analysis Group, Lexington, MA.  Gillette, G. L. and S. J. Treado 1985. “Correlations of Solar Irradiance and Daylight Illuminance for Building Energy Analysis”, ASHRAE Transactions, 91(1), 13 pp.  Lawson, B. 1990. How Designers Think. The Design Process Demystified. 2nd ed. Cambridge, The University Press, pp. 42-43.  NCE 1995. National Construction Estimator 1995.

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