Understanding the Iowa grid emissions status on a daily basis to help ENGIE visualize energy loads.
What it does
Using provided ENGIE data to track resource usage from various sources (Natural Gas, Pellets, Oat Hulls, Coal) to provide daily CO2/CH4/N20 emission data in a simple Pie chart format.
How we built it
Challenges we ran into
- Extracting meaningful data from the Excel spreadsheet without overlapping values.
- Understanding the CO2 emissions conceptually and how it correlates to the energy usage and Electricity purchase pertaining to both the campuses.
- Were not able to figure out the implementation of the API for real-time data as it relates to energy usage within the timeframe given as there were factors we did not account for in our initial brainstorm(e.g extracting data from the APIs for different energy sources being supplied to the boilers of power plants and isolating the root calculations and combining them later on)
Accomplishments that we're proud of
- First and foremost is having a working product that accomplishes Challenge No.1 as provided by ENGIE because if the software doesn't work then no one can use it or purchase it.
- Creating a simple User Interface that is legible and easy to understand and interact with on the Frontend.
- Being able to collaborate and learn as a team and figure out where are our strengths and weaknesses.
What we learned
- The complexity that goes into developing and maintaining energy and electrical grid systems around the world.
- The difficulty in tracking and interpolating the data that comes out of these systems to provide meaningful feedback to the people running these processes on a daily basis.
- The amount of dedication it takes to pickup and learn a new piece of technology and being able to successfully implement and make it work with the final product without breaking any parts that were already functional.
- The importance of communication and documentation understandability between team members.
What's next for ENGIE Dashboard
Developing a plan to implement the API functionality to be able to visualize real-time emissions data on a day-to-day basis for each specific fuel source. Learn more conceptual knowledge on how to create predictive models to be able to implement Challenge 3 as given by ENGIE.