Team: 40
School: Santa Fe Preparatory Sch
Area of Science: Power transmission and generation
Proposal: In the modern day, the implementation of renewable energy sources is crucial to curtailing rampant global warming. While broad calculations have been made predicting the future of renewable energy worldwide, less investigation has been performed as to the implementation of renewable energy on the local level with specific renewable technologies. The particular technologies that will be examined include -- but are not limited to -- solar power, wind power, nuclear power, and hydropower, compared to natural gas and other forms of fossil fuels. We will begin by collecting data from government databases on the production costs, energy outputs, and carbon dioxide emissions of each technology. After that, we will use this data to construct mathematical models predicting the futures of these statistics, possibly with the aid of machine learning. For instance, we would like to see, based on historical data, how well a windmill will perform in ten years, and more importantly, what this means for the energy market in New Mexico. We can extend our conclusions about changes in costs, output, etc. to extrapolate on the implementation of these renewable energy sources in New Mexico. Ideally, our code will also be applicable to other communities as well. Said code will be used to implement the equations we develop and generate tables, charts, graphs, and other tools for visualizing and interpreting our conclusions. These conclusions could then be applied to chart a future of more optimized renewable energy usage.
Team Members:
Sponsoring Teacher: Jocelyne Comstock