Energy Plus _ Tutor. Khee Poh Lam 

One Montgomery Plaza _ Design Builder Modeling

Urbanization of America in the past several decades has fueled a wide range of construction activities. Consequently, the energy consumption of urban masses has increased dramatically demanding the need for the development of an effective and accurate energy simulation program.

This study utilizes Energy Plus, a matured building energy simulation tool, coupled with Design Builder to determine the optimized building performance through evaluation of parametric simulations. The input data uses specifications of intended products to represent the actual condition which effectively produces more accurate results. Additionally, more detailed results of parametric study can be obtained by using Energy Plus through manipulating the final data and adjusting the setup duration.

The baseline building for this study, One Montgomery Plaza, is a typical low rise commercial building located in Norristown, PA. By assuming the location of the plaza to be in Pittsburgh, PA, the weather data could be selected for energy calculations due to the two cities’ comparable climate zones.

The geometric model for Design Builder is divided into three parts: building envelope, glazing system type, and HVAC system. By taking them into parametric considerations, the parameters could be varied by changing the U-value, window-wall-ratio, SHGC, visible transmittance, and HVAC system. Admittedly, consideration was given to the different energy usage patterns, and various HVAC when comparing the baseline building to the proposed design.

With five different case factors; baseline envelope and HVAC, Simple and Spectral glazing, and alternative HVAC and proposed envelope, four different alternatives are generated. With these alternatives, 16 combinations can be attained. Within those parameters we compare three major sets’ evaluation factor and result in the conclusion. Four alternative tasks are conducted based on ASHRAE 90.1 2011, DOE reference building and CBECS benchmark buildings.

General Information and Assumption

For evaluating building performance, it is imperative to put a exat weather data as much as possible. The EPW file format is utilized by EnergyPlus, the energy modeling software developed by the U.S. Department of Energy. EPW files reveals a type of weather file, not a weather dataset. The EPW file is the default format for DOE’s library of weather files for over 2,100 locations – including 1,042 locations in the United States, 71 locations in Canada, and 1,000 locations in 100 other countries throughout the world. The EPW files were compiled from TMY, TMY2, TMY3, and other international datasets. With this data set, it is possible to assume validate weather condition for simulation. Therfore, the precise understanding and data input of weather file is needed. It is also possible to input the weather data directly in Design Builder before modeling building construction and detail. With this we can see the difference. The original building location is 425 Swede St (Airy St), Norristown, PA 19401.


A type of climate defined in the ASHRAE standard consisting of Climate Zone Number 5 and Climate Zone Subtype A. Climate Zone 5A is defined as Cool– Humid with IP Units 5400 < HDD65ºF ≤ 7200 and SI Units 3000 < HDD18ºC ≤ 4000. The heating degree day of Pittsburgh is 5968 hours and the cooling degree day of Pittsburgh is 654 hours. The weather conditions used during a heating design calculation are set according to the building location. 

Typical heating design temperatures lie in the 99th or 99.6th percentile of all temperatures that typical occur at a site throughout the year. A 99.6% design temperature is one that will be exceeded, statistically speaking, for 35 hours in a typical year. All in all, We take 99.6th % design and 1.8 of design margin as our baseline weather condition. And the baseline set-point of each degree day and set-back thermostat set-point is followed by the value of Doe reference.

Thermal Zoning

There are invisible walls in building energy simulation model. The people are not only living in the physical spaces but also occupying the thermal zones made by these invisible thermal walls. How these thermal zones are defined? The answer could be varied based on all the possible heat factors (i.e. programs, orientation, adjacency of window and HVAC control). Based on ASHRAE 90.1 (2010) appendix G, Table 3.2 is applied to One Montgomery Plaza. Since the HVAC zones have not designed for each zone, thermal zones are defined based on similar internal load densities, occupancy, lighting, thermal and space temperature schedules. By doing so, the accuracy of result would be increased and it would be possible to detect which zone consumes more energy.

Base Case Building Enclosure

Basic building construction is based on the data of ASHRAE 90.1 Appendix A, ASHRAE 90.1 Table 5.5-#. Approximately, each R value is followed baseline data in ASHRAE and the envelope construction is based on the existing modeling input parameter, which is already mentioned in part 2. Section 1 and 2 in part2 specified every enclosure parameter and analyzed them based on the existing construction. Additionally, we assumed that the elements of building construction based on brick and curtain wall construction, but the built date and management are quite old and poorly maintained. Thus, the base line was distorted in a way of assuming those typical situations. Overall, in this section we did re-organized selections of every parameter in three basic concepts; poorly maintained and insulated existing facades and roof constructions, fully maintained and insulated envelope, and basic energy code standard. This means that the elements and specification is analyzed with R-value parametric, hence the U-value of each sectors are recommended on top of the basic criteria.

Internal Load

Internal loads from building occupants, lighting, and equipment largely take account for internal heat gain and energy consumption. For the reliable prediction of building performance, assumptions for internal loads should be made with meticulous consideration. For the input assumptions for internal loads, we referred reference standards such as ASHRAE 90.1(2010), DOE reference building, and ASHRAE handbook: Fundamentals. For input variables with no reference value provided, assumptions were made as reasonable as possible. 

In order to make the assumption as reasonable as possible, we divided the whole building by zone type and assigned different internal load factors (occupancy density, lighting power density, and equipment power density) for each of zone instead of assigning average values for office building to the entire building. 

Every thermal zones were categorized into six different types of zone for internal load-assigning purpose; Office, corridor, elevator, restroom, stairwell, and garage. Zone types were first grouped in compliance with regularity of occupancy and then sub-divided by expected lighting power density and equipment power density.


Schedules for occupancy, lighting, equipment, heating / cooling, and HVAC operation, which have the biggest influence on building energy performance were assumed for basic model assumption. Values for input variable were mostly derived from DOE reference building, ASHRAE 90.1. Input variables with no reference (e.g. lighting schedule for circulating areas) were assumed based on occupancy density and schedule.

Base Case HVAC system

VAV system with terminal reheat was selected as HVAC system in the base case model. Variable air system controls thermal zones by changing the volume of supply air, while keeping the temperature of supply air constant. To prevent overcooling occurred by asymmetric load generation in different thermal zones, reheat coil heats the air supplied to the zones with relatively low load in summer season. 

In EnergyPlus models of our study, HVAC systems were designed with compact designing method, which has a benefit of allowing simple input specification and auto sizing. However, we can’t arrange the number and exact location of the air handling unit (AHU), which can result in undesired delivery cost from delivery and terminal units (e.g. duct, damper, and terminal unit) in the simulation result.


Through comparative analyses of alternative models, underlying metrics of building performance simulation and influence of various variables that affect building performance were explored. Basic assumptions were made in accordance with ASHRAE 90.1 2010, DOE reference, and ASHRAE fundamentals 2011. Input values for internal load and schedules for occupancy, lighting, equipment, and HVAC system were kept constant throughout the alternative models. 

Alternative model #1, which was treated as a base case model, was assigned with VAV with terminal reheat, base case building enclosure compliant to ASHRAE 90.1 minimum requirement. Glazing system of Alternative #1 was defined with simplified glazing definition method. 

Alternative #1 was compared with Alternative #2, which was identical to Alternative #1 except for that implemented spectral glazing definition method, instead of simplified glazing definition method. Despite the assigned U-value and SHGC for glazing system were same in both alternatives models, overall EUIs had 2.1% difference, which isn’t a negligible degree. Peak cooling load was even greater reaching -90.8%, which may result in significantly greater HVAC sizing. It was found that though the simplified definition method have benefits in its simplicity and flexibility, spectral definition method is more recommended because it’s capable of covering wider range of glazing types. 

Alternative #2 was compared with Alternative #3, which only altered a type of HVAC system from Alternative #2. HVAC system assigned to Alternative #3 was constant air volume DX (unitary single-zone). The difference between centralized and distribute control was investigated in the comparison. A noticeable difference was found in annual site EUI, which corresponds to a percentage difference of 14.1%. The annual heating energy consumption was 174,418kWh lower (-55% percentage difference) in Alternative #2. In contrast, the annual cooling energy consumption was 1,193,802kWh greater (151% percentage difference) in Alternative #2. 

Finally, Alternative #1 and Alternative #4 was compared. In this comparative analysis, thermal and energy performance were analyzed with regard to changing thermal properties of building enclosure. In alternative #4, proposed enclosure was assigned with much higher overall U-values. The annual EUI difference wasn’t significant. Alternative #4 displayed 2.0% lower Annual site EUI. On the contrary, Peak cooling and heating load was noticeably smaller in Alternative #2(-91.1% and -92.4% respectively), which may lead to significant saving from HVAC system sizing. Another finding was that the perimeter zones showed greater difference in thermal and energy performance between two alternative models. 

The limitation of the study was that overall site and source EUI was unreasonably low compared to typical EUI of commercial buildings. Our team struggled to figure out what the reason is and tried to fix it, but failed to make the result into reasonable range at last. EUI result of modified base case model was 152.68kWh/m2 which was slightly closer to general EUI of office buildings compared to original model. However, the findings from the study is meaningful in that the trend of energy and thermal performance was explainable and reasonable.