eQuest Energy Model: Update 1

Using eQUEST energy modeling software, I was able to accurately predict the energy use of my apartment, and shared wall apartments, in my multi-family dwelling. Here are some glimpses of the process.

Note: I finished this project while watching tonight's Cambridge City Council Meeting broadcast on "City TV". City Councilors are celebrating the ribbon cutting of a new library, while many Boston neighborhoods are reeling after the recent news that four public libraries will be closed in September and others will face budget cuts. 


I digress.


Here are some screen shots from my study:

My Home Location in Cambridge, MA

eQuest has a built-in "wizard" that guides the software user through the modeling process. I chose the simple wizard, which included 50 windows, each requiring entry of several data points. Some of the input data help to define the following:

- Gross floor area
- Partition Layout
- Roof Construction
- Exterior Wall Construction
- Interior Wall Construction
- Above and Below Grade Rigid Insulation
- Window Type
- Window Locations
- Exterior Window Blinds, Shades
- Unit Counts
- Occupancy Duration (day only, night only, 9-5, etc)
- Office Equipment
- Laundry Loading
- Interior Lighting Intensity and Type
- HVAC Equipment Type
- Ventilation Type (Mechanical/Passive)
- Miscellaneous Equipment Loads
- Seasonal Thermostat Setpoints
- Weather Region

Typical eQUEST Window "Building Footprint"

 Typical eQUEST Window "Building Envelope Construction"

Typical eQuest Window "Exterior Windows"

I'll spare you the other 47 data input windows. Some of the data has a significant degree of uncertainty, which results in considerable error. Take for example the rating and existence of insulation. I had to assume an R-value for the existing insulation. I assumed R-15 insulation in the exterior above ground walls and in the ceilings. I also assumed that the building was constructed without underground rigid insulation. 

Another abstract input was the 'infiltration rate' of the the building facade. This is the rate at which air moves through the building envelope (i.e shell). Leaky buildings have high infiltration rates, tight buildings have low infiltration rates. Infiltration is measured in cubic feet per minute (CFM). After doing some research, I picked an infiltration rate that a bit higher than the average, due to the age and observed draftiness of my apartment. If I were to hazard a guess, the infiltration rate is probably higher than the conservative value input.

After completing the data input, the software generated this model:

eQUEST Building Model

Then the software generates a energy consumption report based on weather data for Boston downloaded automatically from a Department of Energy database.

The report looks like this:

Baseline Design Electric and Gas Consumption

 

My first impression is that the report is within the ballpark. I'm going to compare the result with the actual electricity/gas consumption. The tricky step will be asking my neighbors, the other occupants of the multi-family dwelling, for their electricity and natural gas bills. Update 2 will include an analysis of the electric and gas consumption predicted by the eQuest Model contrasted with the actual building use. It would also be valuable to change data inputs, such as the insulation R-values, and see how the predicted energy use is affected. Another idea is to calculate kWh and btu per person, then compare the consumption with that of a typical single family household. 

Stay tuned...