Project Overview, Performance, and Prediction

The two slides above showcase the home screen and then launch the user into the product. The user is given two options. In location ideation, they have the opportunity to receive information on a specific municipality within Miami. Design choices allow the user to pick what kind of physical infrastructure they want to implement in an area such as solar panels or power lines.

This would have taken the projected effects of climate change into account when calculating the type of energy grid infrastructure being built versus the location the user had selected, measuring the resilience factor of the proposal. Assuming that the design matrix used internally by the program was accurate, this would have been an ideal solution with regards to the objectives of this project.

The product will analyze the results discovered and check afterwards if our modules are performing positively, despite possible adjustments. In this context, we will evaluate if the chosen municipality is more prone to extreme weather impacts, such as flooding and high wind gusts, and observe if the location should receive the implementation of one or multiple solar panels. If the iteration does not work properly, then shifts will be input in order to arrive at the best result, without switching our goals if the process needs to be restarted.

The user can also use the platform to see what the current solar panel infrastructure looks like. In the slide to the right, after users input what kind of physical infrastructure they want to be implemented the program generates an expected success rate for their project.

Discussion - Comparison of Prediction to Performance

This model had an issue where the values and weightings of the design matrix were being assumed by our team on behalf of decision-makers, meaning that the output relied to heavily on assumptions made by our team. Without any way for the user of the platform to manually input the weighting of different metrics before using the map, there would be confusion and inaccuracies with the output generated by the system. Even in the case where decision-makers could manually input this, that would overcomplicate the platform and potentially make it unusable as putting so much emphasis on the users' ability to provide background knowledge to the subject and their ability to sharply define what values hold the most importance is unrealistic.

Project Overview, Performance, and Prediction

With insight from our sponsors at Sandia National Laboratories, we learned that considering multiple hazard conditions and energy sources might detour from a single impactful solution. While looking into other renewable forms of energy we learned how wind generation methods aren’t suitable for our area as Florida’s wind speeds aren't high enough to spin turbine blades. Moreover, hydro-energy methods aren’t satisfactory because of Florida’s flat terrain. We wanted to focus on solar panels considering that other forms of renewable energies wouldn't be as fitting and because of how they can be easily retrofitted onto existing buildings.

From this medium-fidelity iteration, we expect to learn more about how stakeholders interact with the product along with testing the information we provide. We are giving our users a determined number of municipalities within Miami-Dade County in which they can iterate. This model is meant to give applicants a basic understanding of what our final product will look like. Within this iteration, the team focuses on creating a product that could be easily shown to stakeholders to invite their responses for feedback related to the municipalities and information we've provided.

Unlike our first iteration, we will not be providing a success percentage because our program will not be sophisticated enough to remit reliable outputs on whether their plans are sufficient for the installation. This iteration highlights how we will be providing information relative to the location regarding climate hazards, costs, and overall information pertaining to the municipality and installation of solar panels. This information will be presented via a pop-out window on the side of the map once the user taps the desired municipality.

Information Gathered

The outcomes from this iteration are to (1) Define a more formalized view of what the platform will look like given the newly revised project scope (2) Enlist feedback from stakeholders so they can give recommendations on possible improvements

The team decided to divide the map using municipalities so users can have a more refined form of information that relates to their local area. On the official Miami-Dade website, it was provided that there are 34 municipalities within the county. We wanted to focus on areas with a population greater than 1,000 people for our product to better suit larger populations. By collecting 2022 population estimates using worldpopulationreview.com we narrowed our municipalities to 24.

After conducting research on maps like the national risk index we had a framework of how data was presented to users along with reviewing the information provided. For this iteration, the team will be providing information on the area that directly impacts solar panels which includes recommendations of placement of these solar panels (roof or ground) and their sustainability in this area. This includes average temperature, average wind speed, average rainfall, sea-level rise along with the projected sea-level rise of the desired area.

Discussion - Comparison of Prediction to Performance

The team is in the process of working on this iteration. We plan on presenting the prototype of our interactive map to an engineer who works as a Resilience/Energy Program Manager at the Office of Emergency Management of Miami-Dade. She can provide feedback on whether the information we're presenting is useful for the installation of solar panels being that she works with plans related to installing them county-wide.