Emissions model

Timing of Electric Loads to Reduce Air Emissions from Power Generation

LEEM, or Locational Emissions Estimation Methodology, is a big data emissions estimation product developed by researchers at Wayne State University in Detroit, MI (USA) and driven by the vision of environmentally sensitive electricity (ESE). The LEEM technology is now commercialized through a WSU start-up, Energy Emissions Intelligence (E2i).

LEEM automatically tracks, organizes, normalizes, analyzes, and reports location-specific, real-time and forecasted marginal emissions information that empowers utility services, energy users, data and energy management firms, government, and individuals to predict emission levels and estimate costs over time and make better, more informed energy and emission management decisions for the future.

LEEM has already been incorporated into real-world projects that help reduce emissions:


Vew April 22, 2016 Press Release

IoT Warez HCS

Poster: A big data enhanced energy emission information system for environmentally guided energy consumption. Carol J. Miller, Caisheng Wang, Guoyao Xu, Mohsen Sadatiyan Abkenar, Audrey R. Zarb, Chang Fu, Shawn McElmurry Presented at the 2016 Big Data Business Analytics Conference. Detroit, MI. 21-24 March 2016.

Video: Smart Energy for a Cleaner Great Lakes

The technology of LEEM has the power to support commercial needs but also provides flexible access for the needs of communities and individuals with three additional products:

1.PEPSO (Polluting Emissions Pump Station Optimization) Learn more

2.HERO (Home Emissions Read-Out) Learn more


For interactive presentions visit the project websites

Locational Emissions Estimation Methodology (LEEM) website


Introduces our real-time emissions calculation tool, Locational Emissions Estimation Methodology (LEEM), that helps reduce harmful air emissions that mix with Great Lakes waters and damage our delicate ecosystem. LEEM offers energy managers, operators, and policy makers a tool to reduce emissions related to electricity without requiring users to reduce energy consumption.

Home Energy Read-Out (HERO) website


Introduces energy users to an accessible mobile platform called Home Energy Read-Out (HERO) that uses LEEM to track electricity-related emissions being released as a result of local energy use. This enables users to time their energy use during times when cleaner energy is servicing the grid.

Linking LMP, Marginal Generating Unit, and Emissions

To estimate the emissions rate from electricity use at any given time and place, the GLPF project team is using an economic approach.

We developed a connection between real-time wholesale market electricity prices (LMPs) and the marginal fuel type. This, in turn, would be used to calculate the emissions rate for generated electricity based on time and location.

For an explanation of our LMP Emissions Estimation Method (LEEM), check out this video: