Renewable Energy with Python, Arduino, and Machine Learning

managers: David gotler and hongchi liu (LEO)

contact: davidhgotler@g.ucla.edu, hongchi@g.ucla.edu

Learning Objectives: Programming, Design and Engineering, Data Analysis

Global climate change is an issue of the utmost importance to be solved by today’s scientists and brightest minds (that’s us!!!). One aspect of this fight is in transitioning to renewable energies. Renewables such as wind and solar are not a constant source of electricity, so they are often supplemented by natural gas power plants in our current electrical grids. We seek to design and build a small-scale, hybrid, Solar-Wind energy cell to be tested for power output and practicality.

In designing our energy system we want to maximize the total power. For solar power, we can test the pros/cons of solar tracking (using light sensors) and “best-guess” (using date/time info) trajectories to optimize the incident light on the solar cells. In recent years, optimizations have been made for designing small-scale wind turbines by making them omnidirectional and focusing winds onto the fans. While optimising the two systems we will also work on linking them to power a main battery storage.

While a system of this size is a drop in the bucket for LA’s energy supply or even a house’s, it could be supplementally used in homes or public spaces to power lights and general electricity needs (backup generators/power, charging phones, wifi, etc.). In analysing the power output we hope to determine the practicality of this application and the scalability for potential applications as an electrical grid (previous research is available on hybrid energy systems).

Here are some relevant links that we will be referring to in the due course of our project –