Energy Modeling: programs and applications:
Understanding the Energy Modeling Process:
Simulation Literacy 101
from The Pittsburgh Papers (2003)
The goal of this paper is to demystify energy modeling for design and facilities-planning professionals. By “energy modeling,” I mean using computer-based tools to simulate the energy use of a building throughout an entire year of operation. This is commonly referred to as “annual energy use simulation.”
The U.S. Green Building Council’s Leadership in Energy and Environmental Design Rating System (LEED™) requires energy modeling to assess the energy use of a building and to quantify the savings attributable to the proposed design. In many cases, architects and building owners are inexperienced with energy modeling and don’t know how to harness this powerful tool to inform the design and decision-making process. Properly used, energy modeling can help optimize the building design and allow the design team to prioritize investment in the strategies that will have the greatest effect on the building’s energy use. This paper seeks to put architects and facility owners in a position to effectively direct the modeling process and evaluate the validity of the output presented.
LEED requires energy modeling if any of the 10 points possible under Energy & Atmosphere Credit 1, for optimizing energy performance, are to be attained. However, energy modeling predates LEED, and there are more fundamental reasons to model. I view the energy model as a continuous process that gets more detailed and refined (and, hopefully, accurate) as the design process progresses. In the majority of cases I have witnessed, the model is made operational so late in the design process that opportunities to use it to guide design decisions have been lost, and it is merely an after-the-fact, record-keeping exercise. It can be a powerful tool in an integrated design process. The following looks at how an energy model might be used throughout the design phases.
During the conceptual design of the project, energy modeling can provide valuable input. In this process, a skilled modeler might quickly assemble a simplified model of the building, perhaps with a single zone per major occupancy type (for example, classrooms, labs, or offices), that can be used to test the effects of site location, building massing, and building orientation. Imagine comparing two design concepts, one a single-story, west-facing building on a flat, open portion of the site, and the other a two-story design partially built into a hillside facing south. Results might look like Figure 1 (page 110).
This kind of feedback is rarely available early in the design process, which is ironic since this is often where the biggest opportunities are! Many modelers are reluctant to build a model when so much is still unknown. Yet good professional experience combined with the default values built into some modeling software (such as eQuest and Energy-10) can quickly yield a model that may not be terribly accurate, but is perfectly adequate for comparing alternate scenarios—relative differences are more important than deadly accuracy.
During schematic design, energy modeling allows those involved in the design process to optimize their focus on the most promising energy-saving strategies. Seeing how the energy consumption of a building breaks down by fuel type, task, and building component allows the design team to focus on the major drivers of energy use. As an example, imagine that the schematic design model shows the output in Figure 2.
Heating overall is a small portion of the energy bill, so the focus can shift to cooling and lighting. It would be wise to look at strategies that reduce cooling loads. We know that if cooling loads drop, then the energy needed for fans and pumps will also drop, because less energy is moved around the building. We also know that lighting energy has to be removed by the cooling system, so reducing lighting energy will also reduce cooling energy. A logical next step is to look at the breakdown of the cooling load for the building, which may look like Figure 3 (page 111).
Since the two largest components of the cooling load are solar gain through the windows and skylights and heat gain from lighting, strategies to examine may include: a different building orientation, daylighting, more efficient lighting, more appropriate lighting levels, better lighting controls, glazing with a lower solar heat gain coefficient, and shading for glazing.
During design development, energy modeling permits parametric studies to be done. Elimination parametrics is a diagnostic technique that allows a better understanding of the energy use of each building component. A series of simulations are done in which one component of energy use is set to zero at a time. When the results are viewed, a clearer picture of how the building uses energy emerges.
Perhaps our early modeling led us to orient our building so that most of the glazing faces south. For some reason (maybe it’s in a historic district), we haven’t been able to include exterior shading on the building. Figure 4 is an example of elimination parametrics for the building:
The larger the difference between the length of the bar for the base case and the length of any subsequent bar, the more that component affects the overall energy use of the building. We can see from this chart that the biggest impact is from changing the solar heat gain coefficient (SHGC) of the window glass, which is the fraction of the energy incident on the glass that gets inside. This result tells us that if we set the SHGC to zero, the building’s energy use would drop by 29%. This building’s energy use is dominated by its cooling load, which is dominated by the solar heat gain through the window glass. The designer should look at the quantity and type of glass, since appropriate shading has been ruled out for other reasons. The second-largest impact comes from setting the lighting energy to zero. This tells us to look at opportunities to reduce lighting wattage, to use better lighting controls, and to see if we can increase effective daylighting without adding more cooling load due to solar gain.
We can see that increasing the insulating value of the walls, windows, and roof from the base case all have modest effects on energy use. This lets us know that additional investment in the insulation is not a priority compared to other strategies.
This result leads us to study whether we have optimized the type and amount of glass in the building, so we should do another type of parametric study. Here we keep everything about the building constant, but vary the area of glass. The base-case building has 700 square feet of south-facing glass. If we choose to look at a standard low-emissivity, argon-filled glazing, the results might look like Figure 5 (page 112).
We can see that there is an optimum area of south glass at around 600 square feet. This may be because cooling load is reduced when the glass is reduced from 700 to 600 square feet, yet 600 square feet is still sufficient to daylight well. If we really like the additional glass for other reasons, we should look at a spectrally selective glazing that lowers the SHGC substantially, increases the R-value slightly, and lowers the visible light transmission slightly. (See Figure 6.)
The more efficient glazing has a different optimum area, and yields a lower energy use at every area studied. The cooling load is notably reduced, and daylighting is still effective. However, the added glazing comes at a cost, both in cost per square foot of glass and in the additional window area. This study enables us to weigh the increased annual savings against the additional up-front cost. Using energy modeling in this fashion often highlights how each additional increment of energy efficiency usually yields a smaller increment of savings. Sometimes savings in other capital systems offset the increased investment (for example, envelope upgrades in a cold-climate building might permit the elimination of perimeter heating).
In a large building with repetitive elements, one way to simplify the modeling process when doing parametric analysis is to model only a representative fraction of the building. On a recent project, a building with approximately 160,000 square feet of office space was modeled using a representative slice of the building that was 1/24 of the actual area (see diagram, left).
During the construction document phase, energy modeling allows comparison of the proposed design to the minimally code-complaint base-case building. This happens to some extent during the modeling for LEED, but the LEED system is hamstrung by using the American Society of Heating, Refrigerating and Air-Conditioning Engineers (ASHRAE) 90.1 Energy Cost Budget Method, so we can’t take credit for all the savings that occur over what might have been the true base case. ASHRAE 90.1’s myriad rules about what we must do for the base case and proposed case underestimate the real savings that result from thoughtful design (see the paper by Dan Nall presented at the 2002 U.S. Green Building Council conference). Outside of the LEED process, we can use modeling to look at the energy-use reduction that results from all of the strategies implemented, including massing, orientation, HVAC-system upgrades, novel control strategies, fan systems optimization, and glazing area optimization.
Understanding the Energy Modeling Process:
Simulation Literacy 101
from The Pittsburgh Papers (2003)
MODELING INPUTS AND ASSUMPTIONS: A model is usually an attempt to simulate the energy operation of a yet-unbuilt project. As such, many unknowns about that building must be estimated for the model. A typical set of modeling inputs might include the following.
• Climate data and
• Interior conditions and setpoints.
• Area, orientation, solar absorptance, and U-value of all opaque building surfaces;
• Area, orientation, SHGC, visible light transmittance, U-value, and shading of all glazing components;
• Mass of building components; and
• Infiltration rates.
• Lighting in watts per square foot;
• Plug loads in watts per square foot; and
• Sensible and latent (moisture) loads from people.
• Lighting schedules;
• Plug-load schedules; and
• Occupancy schedules.
• Heating system type, including the source, distribution, and terminal units;
• Cooling system type, including the source, distribution, and terminal units;
• Ventilation system type;
• Fan and pump inputs;
• Economizers and/or heat recovery systems;
• Domestic hot-water system;
• Specialty systems (commercial kitchens, for example); and
• Renewable-energy systems.
Literally hundreds of inputs will need to be entered to build a model. Some user-friendly programs have built-in industry standard defaults that speed up early model creation. The responsibility for the accuracy of these inputs resides with different team members. The clients must supply their best estimate of the occupancy of the building daily and seasonally, in detail. They must supply location data—weather data if there is no nearby standard weather station data, and all pertinent site features, such as shading of the building by topography, vegetation, or adjacent buildings. The clients should supply a list of plug loads likely to be in the building and their anticipated frequency of use. Nameplate data for wattage of most plug-in equipment will be higher than what the equipment uses, so actual measured data is always more accurate (in one case the mechanical, electrical, and plumbing [MEP] engineer estimated over 10 watts per square foot for plug loads for a building, yet the prospective occupants had a measured usage of one watt per square foot in their existing building. Making an error of this magnitude results in a drastically oversized cooling system, adding useless capital cost). The architect must communicate the building envelope inputs, paying special attention to unusual items such as high-performance glazing, shading devices, or unusually lightweight or massive construction. The lighting designer should be asked to estimate lighting wattage for the different occupancy types. The design team should discuss with the MEP engineer appropriate mechanical system types to put into the model.
If the modeler is the MEP engineer, that person must understand very clearly the difference between an annual energy use simulation, which seeks to model the building throughout an entire year with its typical usage, and design load modeling, in which the goal is to size the heating and cooling system equipment to handle the reasonably expected peak heating and cooling loads. Engineers tend to add safety factors in a lot of places, and the peak design loads they calculate, especially for cooling, tend to result in systems that are oversized. (This is not news….) Oversizing has many penalties for the building owner, including higher capital outlay. The architect or owner who wants to protect the budget should look just as carefully at the inputs and assumptions for the design loads as for the annual simulation. Some things worth checking include:
• What are the assumed outdoor and indoor design conditions? If the peak is designed for an outdoor condition that occurs once a year for a few hours, and simultaneously the system is being asked to maintain 72°F inside, an oversized system will result.
• Is this a building carefully designed to be daylit, yet the model has all the lights on at full tilt while the building is experiencing peak solar input?
• Are there more people in the building than reasonably possible because every space is calculated at peak occupancy, even though those people packing the conference rooms can’t be in their offices at the same time?
With annual simulation, the design team attempts to predict actual building operation, not its peak; schedules and other inputs should reflect that objective.
The people for whom the model is being done (generally the owners or the architects) should ask the modeler for a complete list of all the inputs used for the model and should check to see that they represent the proposed building accurately. They should ask for a document with a table for each major occupancy and all of the inputs for each occupancy. They should ask questions pertinent to their building type; ask, for example, how the model sets ventilation air quantities—does it vary according to occupancy or is it constant volume? And, they should review the inputs before modeling is begun so they can ensure that their intent is accurately represented.
Common errors include:
• Plain old slipped-a-decimal-place data-entry errors (100 square feet of glazing instead of 1000, for example);
• Incorrect lighting and plug-load power densities (usually too high);
• Incorrect glazing characteristics; and
• Peak occupancy in all spaces at once, which virtually never happens. (An example of this is a dormitory in which all student rooms and all public spaces are simultaneously set to peak occupancy. This may lead to the model calculating ventilation air at three or four times the actual amount needed by the occupants.)
The watchword of any simulation process is “garbage in, garbage out.” The team needs to take joint responsibility to ensure that the inputs are reasonable. Don’t get hung up on whether the office lighting will be 1.1 or 1.0 watts per square foot at this stage! I usually ask the modeler to first produce the base-case building model, because the inputs are likely to be familiar and because a good baseline is a solid foundation for all the work yet to come. Also, the energy use of a minimally code-compliant building is more likely to be familiar, so the modeler can more easily evaluate whether the model is sufficiently accurate.
GETTING, AND VETTING, THE OUTPUT
Once the model is built, it’s time to run it and see if the results are believable. Sometimes the output or conclusions are far from physical reality. The modeler should present the output in a form that can be read and understood by the owners and architects. If the person producing the model sends a stack of printouts direct from the modeling software that exceeds the size of the Manhattan phone directory and requires the Rosetta Stone for deciphering the information, recycle the material and ask for a concise document written in English. The output report should include energy use by month and by year for heating, cooling, domestic hot water, mechanical systems, lighting, plug loads, and other sources of electrical consumption (such as elevators). The report should show heating and cooling consumption by building component, telling how much is due to walls, roofs, windows, infiltration, ventilation air, etc. This guides us to look for the areas where we can achieve the biggest savings. The report should include a table of areas for each building component (walls, roof, windows, etc.) as a quick check on the accuracy of the take-offs.
The monthly output helps us vet the validity of the model. If cooling energy rises in the winter, something’s probably out of whack. Getting component output also promotes insight. In a commercial building, for example, infiltration is unlikely to be the largest load amongst the heating components.
One of the most effective ways to vet the model is to become familiar with energy-use benchmarks for typical buildings of the same use in a similar climate. Owners of multiple buildings can build a database of energy use by building type for both the thermal and electrical components of energy use. Architects can investigate the energy use of buildings they have designed in the past (this is good practice in any case, as it serves to inform the goal-setting process in the formative stages of the project.) Databases of building energy use are available from the government (such as the U.S. Environmental Protection Agency’s Target Finder). Some good benchmarks for quickly understanding whether the model is on track include:
• Total annual energy use per square foot;
• Annual energy use per square foot for heating, cooling, and electricity;
• Cubic feet per meter of ventilation air per person of expected occupancy; and
• Square foot per ton of cooling.
If the output seems out of bounds based on past experience, a more detailed look at the model output is in order. Can a physical explanation be constructed for what seem to be anomalous results? On a cold-climate, institutional building, for example, a modeler found no benefit to wall insulation greater than R-6. I asked for, and tried to generate myself, a physical explanation for this unlikely conclusion. No one was able to explain this result. A simple hand calculation of heating energy saved by going from an R-6 wall to an R-19 wall during the building’s unoccupied hours yielded energy savings seven times what the model showed. Something here was off, and no one could explain it physically. Usually persistence will turn up the error.
Once the base building model is operational and yielding believable results, it’s time to set the items to be examined parametrically. Decisions that need to be made about the proposed building are continually informed by the powerful ability of the robust model to answer “what if” questions. Energy modeling has the potential to be highly interactive and teach all involved while funneling the owners’ resources to the places where the most effect can be made.
ACCURACY OF THE MODEL
As the saying goes, the map is not the territory. No model will accurately predict the actual energy use of the building; there are too many variables to control.
• The building may not be built exactly as drawn, which can have an especially large effect on air leakage if that is a significant factor (as it can be in skin-dominated buildings).
• The occupants will use the building differently than predicted. They may use the building for more hours or use less equipment, for example.
• During extreme years, the climate may vary 20 to 30% from that modeled.
The value of the model is its ability to compare alternate schemes and show the resulting differences in energy use. The differences will tend to be more accurate than the absolute values. Don’t sweat the petty things (or was it don’t pet the sweaty things?).
LIMITATIONS OF THE SOFTWARE
Many different software packages are available. Some are sold by vendors of HVAC equipment, some are free from the government or electric utilities, and some are privately written and sold. None is perfect, and it’s important to understand the unique limitations of each.
As much as possible, know whether the software proposed for modeling is appropriate for the building that is going to be created. If a good portion of the building will be below grade, can the software handle that well? If daylighting is a key strategy, will the model have enough daylight-modeling capability to be able to turn lights down when daylight is available? If the building will use advanced HVAC systems, such as structurally integrated radiant heating and cooling, can the software model this explicitly? (Very few can.) Can the model produce comfort criteria such as mean radiant temperature in occupied spaces? Can it model natural ventilation and enthalpic heat recovery? Most of the modeling programs commonly used have a hard time with many of these situations, so the rule is buyer beware: make certain you know what you’re getting before you commit significant resources to the modeling effort.
Marc Rosenbaum, P.E.
Weatherization under $500
1. Minimize usage of end product, reduce consumption (low flow). Reducing
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RetScreen Based Case vs Proposed Case
Manual-J | Manual J | Heating and Cooling Load Calculation – An Introduction
by Home Energy Partners on March 17, 2011
Manual-J Heating and Cooling Load Calculations
Manual J is the name for a specific protocol (often called “Heat Load Calculation” or “Cooling Load Calculation“) used to determine how much heating/cooling a home needs to stay cool and dry in the summer and warm in the winter. This load calculation process was developed by engineers in the heating and air conditioning industry and has been used for decades to accurately size heating and air-conditioning equipment. After completing this load calculation process, one can choose a properly sized piece of machinery to satisfy the load.
Why is a properly sized HVAC system important?
What does a Manual-J Load Calculation report actually provide?
A Manual-J load calculation report provides three main pieces of information regarding heating and cooling load:
Block load calculations versus room-by-room load calculations:
This is an important concept to understand, because it can make all the difference in the performance of your system. Fortunately, it’s an easy concept to describe and understand. It goes something like this: