Team: 11
School: Santa Fe Preparatory Sch
Area of Science: Environmental Science/Optimization
Proposal: Today, renewable energy is receiving more attention than it ever has before. However, the main argument against it is that most sources do not provide a consistent flow of energy. Solar panels do not function if it is dark or cloudy, which can be highly detrimental to an effective and economically viable renewable alternative. With this project, we would like to expand on our project from last year, Optimizing the Geographic Location of Photovoltaic Panels in the Contiguous US. In addition to expanding our existing model, we would like to consider panel placement within existing energy infrastructure and consider energy consumption demand.
This project will be based on Python and will aim to expand upon our existing model, namely improving the accuracy of our energy generation predictions by using a more applicable regression model. More factors will be included, such as relative humidity, horizontal irradiance, temperature, solar zenith angle, altitude, and other important factors not considered in last year's project. Additionally, we will aim to further optimize photovoltaic panel placement incorporating existing energy infrastructure and energy demand throughout the contiguous US.
With the increasing attention toward the potential of renewable energy, the ability to predict the most viable solar solution would be a powerful tool, which this project is aiming to create.
Participants: Ian Olson, Lucas Blakslee, Shrey Poshiya
Team Members:
Ian Olson
Shrey Poshiya
Lucas Blakeslee
Sponsoring Teacher: Jocelyne Comstock