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Report for:

Green Building Alliance – Northside Coalition for Fair Housing

James Construction Project #: 15-006 IES reference #: 12468

Masterplanning & Smart Community Lifecycle (SCL) Services

Report By: James Construction

and Integrated Environmental Solutions Limited

International Sustainability Consulting Developers of the IES <Virtual Environment>

March 06, 2015

Contents Introduction ..............................................................................................................................................................2

Building Energy Simulation Methodology ................................................................................................................3

District Area Descriptions and Summary – General Analysis ...................................................................................5

RFP Target Area Description and Summary – Detailed Analysis ..............................................................................9

Task 1: Energy/EUI Simulated Calculations and Report ....................................................................................... 12

Task 2: Solar Study Analysis and Report ............................................................................................................... 16

Task 3: Wind and Airflow Analysis and Report ..................................................................................................... 19

Simulating Atmospheric Boundary Layer .......................................................................................................... 19

Appendix A: Monitoring-Based Commissioning Activities – Description of Capabilities ..................................... 25

Example 1: Airport Lounge ................................................................................................................................ 26

Example 2: US School ........................................................................................................................................ 27

Example 3: Shopping Mall ................................................................................................................................. 28

Appendix B: Water and Stormwater Study – Description of Capabilities ............................................................ 31

Appendix C: Walkability and Transportation Network – Description of Capabilities ........................................... 33

of 35 www.iesve.com UNITED STATES OF AMERICA | UNITED KINGDOM | IRELAND | CANADA | INDIA | AUSTRALIA

Introduction This report provides the information produced for each of the tasks as agreed upon by James Construction and IES listed in the work order dated October 19th 2014 under the “District Energy” section of the Work Plan produced by Pfaffmann + Associates. As a product of recent and ongoing R&D, IES are developing two new software tools for Masterplanning and Smart Cities. These products are part of a bigger R&D project called the Smart Communities Lifecycle (SCL). A summary of the two new products follows:

• PrioCity (working title): This is a web based tool that allows the user to model a whole city (i.e. A City Information Model or CIM) and can be used on a desktop, tablet and Smart Phone.

• SketchUp Masterplanning plugin: This is a desktop based tool that allows the user to operate with smaller district or neighborhood models for masterplanning purposes. These masterplanning models can be connected to the IES Virtual Environmental in order to conduct various analyses e.g. energy and carbon building simulation, solar studies, daylighting, airflow circulation, etc.

Both of these products are continuing to be developed and will increase in interoperability between each other and other technology such as GIS and BIM systems. For the scope of work delivered for the Northside Coalition project (refer to fig 1), IES developed models only through the Masterplanning Plugin. Copies of the report, analysis, and videos are located at: http://www.jamesco.com/ncfh/

http://www.iesve.com/

http://www.jamesco.com/ncfh/


Building Energy Simulation Methodology

You can see the areas that have been modelled in detail. For each building the building shell has been converted to a zone/floor model with number of floors, floor to floor heights and percentage glazing defined for each building. At this masterplanning level it is possible to select adapt each building model to be suitable for a range of building simulation analyses within the IES Virtual Environment (VE). More detailed building simulation can be undertaken within the VE or within SketchUp or Revit and simulate within the VE. For masterplanning purposes a model has been built as follows:

• From the footplate or shell of the building use the masterplanning interface to build a model that has the correct number of floors, floor to floor heights and percentage glazing. This has been done for the buildings shown above.

• When assigning attributes select the room type for each floor from a dropdown menu. This contains representative ASHRAE information to define the typical room type in terms of occupant behavior, energy use, etc.

• Assigning typical ASHRAE constructions from a dropdown list for roofs, ceilings, external walls and windows, etc.

Simulation analysis focuses on the following

• Simulation 1 - Baseline Simulation – typical construction assumed for age • Simulation 2 - Improved Fabric Simulation – improved fabric construction assumed for age



Figure 1: The chart below details key construction materials entered into the energy model for the baseline and improved simulations. These construction materials have defined attributes that determine their thermal performance.

Multi-Family Baseline Multi-Family Improved

HVAC Service Central Heating (No Change)

Doors Wooden (No Change)

Interior Walls 25mm Gyp, 100mm Air, 25mm Gyp

(No Change) R= 4.11

Ceiling Construction Timber Joist Sloping Roof - w/ Loft

R = 4.51 R = 4.51

Roof Construction Sloping Roof - Domestic Sloping Roof - w/ Loft

R = 1.68 R = 35.73

Ground Floor Uninsulated Suspended Timber Insulated Suspended Timber

R = 9.04 R = 14.72

Exterior Walls Timber Frame Standard Wall

R = 12.64 R = 16.25

Exterior Windows Single Glazed Double Glazed

U=.847 U=.503



District Area Descriptions and Summary – General Analysis Figure 2: Identifying 3D models and form generation – model overview

Figure 3: A total of 274 buildings were identified in the total site area



Figure 4: Building area calculation yielding a total of 21.02 hectares or 2.262M ft2 of building space

Figure 5: Underlying land use: Residential, Commercial and Sports Arena within project boundaries Residential

Commercial

Sports Arena



Figure 6: Colors indicating building types: Multi-Family Dwellings, Office, Garage Parking, Post Office, Retail, Single-Family Dwelling, Sports Arena, Warehouse

Multi-Family

Office

Parking Garage

Post Office

Retail

Single-Family

Sports Arena

Warehouse

Figure 7: Colors indicating total number of stories across project site ranging from 1-4 stories

1 Floor

2 Floors

3 Floors

4 Floors



Figure 8: Colors indicating building percentages of glazing to skin ratios ranging from 5%-25% with an average of 18.5% across the site

0-5%

5-10%

10-15%

15-20%

20-25%

NOTE: Other model visualizations and images can be found in the PPT file submitted under separate cover.



RFP Target Area Description and Summary – Detailed Analysis IES was tasked with a specific site area to continue a full detailed analysis. The area is indicated below and subsequent supporting details to follow.

Figure 9: Defined area for detailed analysis

Figure 10: Defined area for detailed analysis highlighted in RED; buildings outside the site in BLUE 274 total buildings within site 54 buildings total in selected site area



Figure 11: Colors indicate building by type: garage parking, retail, single-family dwellings and warehouse

Garage Parking

Retail

Single Family

Warehouse

Figure 12: Colors indicate building housing type – RED as semi-detached; BLUE as detached housing

Detached

Semi-Detached



Figure 13: Colors indicating which address codes to specific street names within the model California Ave. Morrison St. Sedgwick St. St. Ives St. Kirkbride St. Lamont St. A St. Lysle St. Brighton Pl. St. Marks Pl. Lamont Way B St.

NOTE: Full model visualizations, models and images can be found in the PPT file submitted under separate cover.



Task 1: Energy/EUI Simulated Calculations and Report Examples of building simulations to indicate the type of data that can be generated and stored within the masterplanning model are shown below. Examples of simulated energy metrics are: Heating, DHW, Cooling, Auxiliary, Lighting, Equipment and EUI.

Figure 14: Three buildings selected at random to begin showing the simulated EUIs and their resulting values

Figure 15: Full year’s heating demand across 54 buildings Each building colored to the amount of heating demand over the year RED is hot/high BLUE is cold/low



Figure 16: Energy Use Intensity (EUI) calculated and indicated by color map Low (BLUE) = 6.06 MWh/yr High (RED) = 118.56 MWh/yr



EUI comparison across the 54 buildings within the focus area, each specified by building address, types, building numbers and the resulting EUIs as a baseline model compared to improved models.

Baseline Model indicates basic constructions;

Improved Model assumes higher constructions ratings and thermal envelopes, as defined on Page 4:

Figure 17:



NOTE: Refer to EXCEL file for full legible tables and EUI comparison analysis.

Deliverable: EUI Comparison tables Energy/EUI Simulation Conclusion: Current Total EUI = 9,061.20 kBTU/ft2/yr

Technically Possible Total EUI = 4,116.47 kBTU/ft2/yr

Current Median EUI = 109.53 kBTU/ft2/yr

Technically Possible Median EUI = 48.48 kBTU/ft2/yr

Total improvement = 55%

Figure 18:

The data below is contextual for comparison of peer buildings for median energy type of buildings in the northeast.

Commercial Residential

CBECS, Commercial Northeast 91.00 kBTU/ft2/yr CBECS, Residential Northeast 63.00 kBTU/ft2/yr

Energy Star 68.25 kBTU/ft2/yr Energy Star 47.25 kBTU/ft2/yr

USGBC LEED (Average of Silver) Certified 62.00 kBTU/ft2/yr USGBC LEED (Average of

Silver) Certified 62.00 kBTU/ft2/yr

Passive House Certified 38.00 kBTU/ft2/yr Passive House Certified 38.00 kBTU/ft2/yr

Net Zero Buildings 0.00 kBTU/ft2/yr Net Zero Buildings 0.00 kBTU/ft2/yr



Task 2: Solar Study Analysis and Report

Figure 19: SunCast solar radiation across buildings within site boundary with a 10m x 10m grid for further analysis and reporting at a granular detailed level

Figure 20: Gradient colors indicate the most amount of solar intensity on any given surface across an entire year Range High (RED): 1377.25 kWh/m2 Range Low (BLUE): 72.25 kWh/m2



Figure 21: Solar intensity on any given surface on January 15th Range High (RED): 0.86 kWh/m2 Range Low (BLUE): 0.09 kWh/m2

Figure 22: Solar intensity on any given surface on July 15th Range High (RED): 7.83 kWh/m2 Range Low (BLUE): 0.10 kWh/m2



NOTE: Additional screenshots in PPT file; roads were added for context but excluded for solar analysis and calculation within model Deliverable: Model solar analysis three videos: (1) Full year, (2) January 15th and (3) July 15th Solar Studies Analysis Conclusion: The following is a detailed Solar renewable analysis within the target area. This analysis determines the renewable electricity produced from one typical domestic system depending on PV orientation, the results of which will determine which homes provide the most impact to the following metrics: Total Solar renewable availability, impact to current EUI, impact to future EUI, and potential district EUI. Figure 23: Based on 13% efficient PV cells

PV generated electricity – West

Orientation

PV generated electricity - South

Orientation

PV generated electricity – North

Orientation

PV generated electricity – East

Orientation

kWh 1978.90 2390.30 1466.10 1992.20

BTU/hr 6,752,007 8,155,704 5,002,333 6,797,386



Task 3: Wind and Airflow Analysis and Report Simulating Atmospheric Boundary Layer In an atmospheric boundary layer, wind speed increases with height due to the influence of surface roughness (i.e. the presence of buildings, trees, roads etc. on the ground), see Figure 1.

Figure 24: Typical velocity profile of an atmospheric boundary layer In the current CFD modelling, the velocity profile was generated according to the parameterised ASHRAE methodology described below. This allows for different wind profiles across various terrain types: Open country; urban; and city centre.

The wind speed UH at height H above the ground is given by:

𝑼𝑯 = 𝑼𝒎𝒎𝒎 �𝜹𝒎𝒎𝒎𝑯𝒎𝒎𝒎

�𝒂𝒎𝒎𝒎

�𝑯𝜹�𝒂

… … … … … … … … … … … … … (𝐸𝐸. 1)

Where, a = Exponent in power law wind speed profile for local building terrain δ = fully developed strong wind atmospheric boundary layer thickness (m) amet = Exponent for the meteorological station δmet = Atmospheric boundary thickness at the meteorological station (m) Hmet = Height at which meteorological wind speed was measured (m) Umet = Hourly meteorological wind speed, measured at height Hmet (m/s) The parameters for different types of terrain are given as in table 1.



Figure 25: Atmospheric boundary layer parameters Terrain Category

Description a δ

1 Large city centres 50% of buildings above 21m over a distance of at least 2000m upwind.

0.33 460

2 Urban, suburban, wooded areas. 0.22 370

3 Open, with scattered objects generally less than 10m high. 0.14 270

4 Flat, unobstructed areas exposed to wind flowing over a large water body (no more than 500m inland).

0.10 210

For the current project, we used the atmospheric boundary layer corresponding to the terrain category 2 i.e. urban/suburban/ wooded areas. The met data was taken on category 3 terrain at a height of 10m. Following is a sample of airflow analysis carried out over the Northside neighborhood. The wind was assumed to be at 3.93 m/s. with a westerly heading. The wind speed was the average of the wind speed recorded as per the weather file ‘USA_PA_Pittsburgh.Intl.AP.725200_TMY3.epw’. Figures 22 to 27 show the various images captured for the wind analysis of the site.



Figure 26: Lengthwise view of the velocity vectors overlaid on plot of velocity contours




Figure 30: Plan view of the velocity vectors overlaid on plot of velocity contours

Figure 31: Plan view of the velocity vectors overlaid on plot of velocity contours

The analysis can be further expanded by including more wind directions. This will allow us to have a complete overview of the wind’s effect on all directions, help us identify any dead spots and ensure better installation of the renewables.



Deliverable: Wind directions, speeds and images provided as part of the report. Further analysis required for siting specific locations for renewables across site. Wind and Airflow Analysis Conclusions: The following is a detailed Wind renewables analysis within the target area. This analysis determines the renewable electricity produced from one typical domestic system depending on turbine size, the results of which will determine which homes provide the most impact to the following metrics: Total Solar renewable availability, impact to current EUI, impact to future EUI, and potential district EUI. Figure 32:

Wind generated electricity – 5

kW turbine Wind generated electricity – 3

kW turbine Wind generated electricity – 1

kW turbine kWh 755.10 453.10 151.00

BTU/hr 2,576,401 1,545,977 515,212

The Power Curve below was used to determine the power output of each turbine:



Appendix A

Monitoring-Based Commissioning Activities – Description of Capabilities

ERGON an IES Cloud service allows you to import, manage and interrogate real building profile or schedule data (down to 1 minute time steps) for use within your VE simulations. You can utilize measured data from the actual building you’re investigating to create profiles that enhance model calibration. Or use normalized benchmark data from other buildings of its type. Such profiles can be used to improve operational models or help close the performance gap by bringing design models closer to reality.

As ERGON is based on the cloud it enables you to manipulate vast amount of data and create profiles that go down as low as 1 minute time steps. Therefore you can not only deal with 8760 profiles, but also 525600 profiles!

Such profiles can be used to:

• Investigate the impact of retrofit options using real building data • Undertake Post Occupancy Evaluations

o Correlate Digital Meter Data o Correlate Weather Station Data

• Improve operational models for performance contracting • Aid in delivering a seamless handoff from construction into building operation • Undertake Monitoring Based Commissioning for LEED V4 • Undertake LEED Measurement and Verification • Help close the performance gap by simulating designs closer to reality

Within ERGON users can easily import .csv files, graphically interrogate the completeness of data, undertake some initial analysis using in-build analytics, and export as a Free Form Profile Data file (.ffd) into ApPro for use in VE Apache simulations. Users can also generate from scratch bespoke profiles at deep granularity (consisting of up to 10 daily profiles which can be assigned hourly across a year).

Within the VE you can link the profiles or schedules created to Master Templates and Design Options functionality to build up libraries of benchmark data for use in other projects to help close the Performance Gap.



Example 1: Airport Lounge IES had been asked by an airport operator to apply our technology to their site. They expressed concern over an airport lounge which operated 24 hours/day and the operators felt was not operating well because of high cooling demand, even in winter. The building was in Glasgow measurements in January. Constant volume, fresh air, fan coil units to maintain space conditions. Fresh air was heated up to the supply temperature of 20°C and supplied to meet design temperature of 21°C.

January 30: Dark Green line is lounge air temperature and cooling value open to maintain room temperate at about 23°C. IES looked at data and told them to reset their supply temperature. They set to 17°C. Immediately lounge air temperature falls to 21°C; cooling requirements to lounge reduced dramatically and fresh air heating load reduced substantially. ROI – minutes.



Example 2: US School Compare case study before IES’ enhanced calibrate model capability. An IESVE user was doing an M&V study on a school in Boulder, CO. The energy consumption had suddenly increased inexplicably. They got early version of our increased analytical capability. Below is the ‘heat map’ information of the gas consumption at 30 min intervals for each day in the year. The upper heat map is predicted values from the IES VE, the lower heat map is the measured values. Note during summer the system switched to a manual process due to the intermittent use of the school.

There are good similarities between the measured and predicted values until November. In November the building appears to have 24 hour gas usage whereas the predicted value is suggesting the normal pattern. This information led to a site investigation when it was found that the system had been switched to manual-on and not automatic. As no one was in the building overnight no one knew the system was operating 24 hours. The system was switched to automatic and resumed to normal operation. ROI: One day (model already built, comparative study + interrogation < a day)



Example 3: Shopping Mall A Scottish shopping mall operator asked for IES’ assistance to look at two performance issues in their mall:

• Excessive energy consumption in throughout the mall • In one part of the mall there were significant customer complaints that the mall was very cold.

Many customers would not go to that part of the Mall. Issue 1: Excessive Energy Consumption In looking at the BMS data we saw the following:

• The blue line is the mall temperature and the orange area is where the mall temperature is

above the design temperature. • The red line is the energy consumption to the mall. • The yellow highlight is showing where we have energy consumption and the temperature is

above the design temperature This clearly shows that there is energy consumed when it is not needed. We identified that if no energy was needed in this part of the Mall the savings would be over $28k per annum. On investigation it was found that the temperature sensor for the mall was not in the mall! The client is currently investigating how best to sort this problem as part of a retrofit to the mall.



Issue 2: Cold Conditions in part of the Mall Through reviewing the BMS data we confirmed the cold conditions in the mall, see below:

• The blue line is the mall temperature which is clearly not meeting the design conditions i.e.

the back dotted line. • The red line is the energy supplied via warm air to the mall. It is clear that this energy is not

impacting on the mall temperature. We asked the onsite engineers to check the dampers and they were found to be locked shut. The diagram below shows the consequences of fixing the dampers.



• The blue shading is the three days prior to fixing the dampers. You can see that the mall temperature is cold and below the set point for most of the time with substantial amounts of energy being supplied.

• The green area is what happened after the dampers were fixed. There is substantial reduction in energy consumption but you will note that the same phenomena now occurs as in the rest of the mall – the use of the wrong temperature sensor for the mall is observed.

In this case study the first issue (wrong sensor) has always been a problem and no one has picked it up. The faulty damper has been a problem for well over a year. IES identified these problems within a few days of getting the BMS data for the building. Proposal: consider having IES collect BMS data for the larger commercial buildings and import into the existing model to identify waste issues.



Appendix B

Water and Stormwater Study – Description of Capabilities

Using IES Masterplanning technology, large scale models can be used to generate water metrics such as water runoff and collection from hard surfaces, pavements, and roads, as well as water collected for various pervious materials as well. The metrics generated link to several rating systems such as LEED related to stormwater management, collection of rain/grey water onsite and water reduction. Examples of the types of reporting generated from the model can be found below. These are provided as an example of what can be adapted for the Northside Coalition project should further work in this area be explored.



Water Efficiency – Water Reduction

Proposal: (1) consider modeling site constraints, permeable and impermeable surfaces to determine water collection potential for site reuse. (2) Apply water efficiency scenarios to determine water and cost savings throughout the buildings and across the site using the model for planning purposes.



Appendix C

Walkability and Transportation Network – Description of Capabilities IES performed a transportation study for the City of San Francisco to understand the connection between heavy rail CalTrain and light rail Muni train network. Using IES technology to better visualize true walk path using the City Information Model indicates a disconnection between transit lines.

Using a 400m walkpath from three key Bart/Muni stations through the central corridor of San Francisco, virtual avatars (indicated by red box below) travel along roads, sidewalks and passenger corridors to provide walking distances around buildings serviced from specific mass transit stations. This tool enables urban planners to properly layout transit networks to connect as large of a region as possible for the general public who rely on mass transit for transportation.



In a developed city like San Francisco, there are obvious areas of disconnect between the rail lines as indicated above that could then be connected with additional bus lines. However, in a new city layout, the project team could use this information to more accurately design a city-wide transportation network, eliminating unnecessary overlaps and covering gaps between transit hubs to cost effectively design the most appropriate system solution.

Proposal: consider using IES transportation/walkability tools to identify the most efficient layout for transit lines to maximize the service to local residence within the site area.


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