July 11th 2023

Hourly Carbon Emissions Factors in IESVE Software

Hourly Carbon Emissions Factors in IESVE Software

Whole Building Lifecycle carbon accounting has three requirements:

  1. Embodied (initial) carbon of materials and processes to construct a building
  2. Operational carbon emitted to heat, cool, and power it
  3. Carbon required to demolish, dispose, renovate, or repurpose at the end of its useful life

This post will focus on accurately accounting for operational carbon during the useful life of the building.  This requires an understanding of accounting for emissions from the electrical grid.

Hourly electrical grid emissions are generally considered in two different ways: average and marginal.

Average emissions look at the total source emissions factor over a given period of time and provide the average emissions due to the source of the power generation.  If at noon on a summer day, the electrical generation is 50% solar PV and 50% coal, the average emissions would be calculated by blending the two sources together for the time interval in question.  The calculated carbon emissions ratio is then applied as an average to every hour. Average can be specified to be daily, weekly, monthly, or annual average.  The most commonly available average data is monthly average or annual average.  By default, IESVE uses average annual carbon emissions data from the Energy Information Agency (EIA).

Marginal emissions refer to how the source energy mix changes in response to demand on the grid.  As more power is needed by more buildings on the grid, new sources of power generation may need to be turned on.  In the example above, a gas-fired turbine may be turned on to meet a change in demand which would shift the CO2e emissions to factor in solar PV, coal, and gas generated electricity.  Thus, depending on the building demand and the electrical grid capacity, adding peaking power to the grid can often change the emissions due to the dynamics of power consumption and available power generation.  This short-term peak power generator often changes the CO2e of the grid more dramatically than only considering the average emissions.

When to use Average vs. Marginal Emissions
Estimating whole building operational carbon Average
Comparing design options Marginal
Evaluating fitness of a new building technology Marginal
Designing a grid harmonized building Marginal

Sources of Carbon Emissions Factors

Utility electrical grids are rapidly changing from fossil fuels to renewables.  Since most buildings connect to an electrical grid, having up-to-date grid emissions factors significantly impacts how much operational CO2e is forecast to occur over the life of the building.

There are several sources of grid emissions factors available today including forecast.  For these reasons, IESVE Software does not “fix” or “pre-define” the marginal rates – with the exception (as of this writing) being the rates used for California Title 24 Compliance which are set by the California Energy Commission (CEC) and described here.  Rather, IESVE Software provides the user with a simple workflow to translate the carbon emissions datasets of their choice for use in energy simulation at any stage in the design process – or in post occupancy. 

There are several different data sets available that look at historic emissions as well as forecast emissions scenarios with different assumptions used in those forecasts.  The hourly marginal emissions data used for this post was supplied by WattTime.  The average hourly data used in this post is pre-loaded into IESVE and comes from the Energy Information Agency (EIA).

Applying Hourly Carbon Emissions Factors in IESVE Software

There are 6 steps to assessing hourly carbon emissions of a building or design option in IESVE using the existing script:

  1. Source the carbon emission data using the appropriate carbon emission type (marginal or average for the site location. Verify the day of the week for January 1st for the carbon emissions data aligns with the day of the week for the energy simulation.  Tip: Day of the week for the energy simulation can be changed in ApLocate by adjusting the starting year.
  2. Ensure the data is in a readable .CSV format.
  3. Convert the .CSV data to .FFD using the script in IESVE and set the category to “Emissions Factors”.  Convert units if needed.
  4. Open the .FFD file in Apache Profiles (ApPro) and categorize it “Emissions Factors”
  5. Apply the .FFD to the Energy Sources and Meters dialog in Apache
  6. Simulate the annual operational performance of the model and use VistaPro to view and export carbon emissions results.

Step 1: Source the emissions data needed for the simulation analysis.  In this example, hourly marginal grid emissions were supplied by WattTime, ensuring the emissions data aligns with weather data in the annual energy simulation.

Step 2: In the Navigators tab of IESVE Software, select the VE Scripts from the pull-down menu.  Click on the link for “Create Annual Data for Free-form Profile (.csv file)”.  This script will generate a CSV file in the appropriate format.  Once created, open the CSV and copy/paste the emissions data into the appropriate column.

Step 3: Once the CSV file is populated with the relevant emissions data with the correct units, use the script “Create Annual Load Free-form Profile (.ffd file)”.  This step will convert the CSV into a usable profile that can be read and applied by IESVE.

Step 4: Open the Apache Profiles Database (ApPro) and filter the profiles by pattern to show only the Free-form Profiles.  Select the profile, rename it, and categorize it under Emissions Factors.

Step 5: From the Apache application, open the Energy Sources and Meters dialog.  Select the appropriate energy source (in this case Electricity) and click on the CO2 emission factor pull-down:

At the bottom of this dialogue, select Hourly CEF and select the Apache profile previously created.  Note: Once the profile has been created, it can be easily shared across multiple projects and/or templates.

Step 6: Simulate the annual operational performance of the model.  Once the simulation is complete, select the VistaPro application and select the simulation results file.  Filter the Categories to show Carbon variables.  Select and plot in a chart, or show the hourly data table.

Hourly carbon emissions for the building are plotted in VistaPro below using an annual average emission factor (top) and a marginal hourly emission factors (bottom):

The difference in hourly average emissions compared to hourly marginal emissions is significant.  Note spring is much lower in carbon emissions than late summer.

The above bar chart compares the total annual emissions over the course of a year for the building when using either the annual average (red) or the marginal (blue) emissions rates.  On an annual basis, using marginal emissions shows a reduction in carbon emissions of about 15% when compared to annual average.  This additional reduction in carbon emissions can play a significant role in building rating system compliance points including LEED v4.1.  When using the IESVE ASHRAE 90.1 Navigator, the marginal carbon emissions, source energy, and site energy can all be easily compared to the automatically generated baseline building.

This comparison shows that while average emissions can be useful for estimating the annual operational carbon emissions of a building, the marginal emissions data shows a more significant reduction in carbon emissions for the same time period.  In addition, the marginal emissions results show the times of year and times of day when the building is effectively emitting more carbon than the average would indicate.

Above: Detailed breakdown of energy end uses in this example project.  Note natural gas has not been removed from this building yet the marginal emissions factors still play a significant role in reducing the estimated carbon emissions.

In the above plot, we isolate one week in July in VistaPro to compare the difference between annual average and marginal hourly emissions for this example building using both average and marginal.  The red areas indicate where the average emissions are higher and the blue areas indicate where the marginal emissions are higher.

Analyzing marginal emissions helps identify which strategies are likely to have a more significant impact on operational carbon because this highlights the times of day and year when the grid is cleanest and dirtiest.  We can then focus on studying strategies and technologies which will focus on the dirtiest times of the year and address those first to have the largest impact on reducing operational carbon.  For example, instead of using site PV to power the building during times when the grid is relatively green, we can instead use the clean grid during those times to power the building and use the site PV to charge a battery for use when the grid is dirtier in the evening.


Emissions data is changing rapidly as many utilities are updating their power generation sources to include more renewable energy sources.  Using current carbon emission factors in evaluating design options and whole building lifecycle carbon analysis can significantly impact design decisions.

Both average and marginal emissions are useful in building design.  Average is generally used for looking at total lifecycle carbon.  Marginal is generally used for assessing building energy conservation measures and identifying the best building design strategies to address the times of the year when the grid is dirtiest.

For a much deeper dive on average and marginal emissions, refer to the following resources:

Marginal emissions – what are they and how to use them

Is your goal real-world impact?

Combating Climate Change by Measuring Carbon Emissions Correctly

Some common of sources of grid emissions data:

WattTime: https://www.watttime.org/get-the-data/

NREL Cambium Viewer: https://scenarioviewer.nrel.gov/

ElectricityMaps.com: https://app.electricitymaps.com/map

ASHRAE Standard 189-2020 Addendum m.  This Standard is updated regularly and should be consulted for each new project.  The emissions rates listed in this Standard are sourced from the Energy Information Agency (EIA).  The default annualized average emissions rates used in IESVE are based on this same data from EIA.