Things you'll learn: MechE, ECE, HVAC systems, data science, Python, data visualizations, working with external partners

The BR sub-team will work to optimize the daily operations of Cornell University's Biomass Grinding facility by creating 1) a machine that will dry biomass particles evenly and efficiently, and 2) a system that keeps the facility at a reasonable temperature. Ultimately, the team will aim to reduce the facility's energy consumption and operating costs, utilizing guidance from contacts in the BEE, CEE, and ECE departments as well as the Campus Sustainability Office. For the Fall 2023 semester, the team is considering starting an additional project relating to lighting retrofitting. It has good potential in creating a direct connection to Earth Source Heating and expanding the team in terms of new recruits, since it involves specific data analysis and computational implementations.

Things you'll learn: Python, Data Analysis/Science, Policy, Communicating with Partners, CSS, HTML, Plotly Dash

Last semester, the BPA sub-team collaborated with the City of Ithaca 2030 District to develop an energy consumption dashboard for various buildings throughout Ithaca. Using data provided by the city and leveraging Python along with Plotly Dash, we built a fully functional tool that enables the 2030 District to monitor and track energy usage across the city. This semester, we are focused on finalizing updates to the dashboard to enhance its functionality and usability. In addition, we are undertaking a new project with Cornell University to implement electric scooters on campus. This initiative involves close collaboration with the Cornell Transportation Office, the Cornell Sustainability Office, and Veo—the company selected for the scooter implementation. If successful, we will conduct data analysis to assess the energy savings resulting from the introduction of electric scooters.

Things you'll learn: HTML, CSS, Python, Pandas, stats, data science, data visualization, design, Figma, Plotly Dash, databases, APIs, working with external partners

In partnership with the Campus Sustainability Office, the FHED sub-team aims to reduce the usage of high energy demanding equipment at Cornell. Particularly, FHED has been focusing on fume hoods, which are some of the most energy intensive devices on campus since they replace heated air from inside the room with cold air from outdoors. Their goal is to create a web dashboard that displays real-time energy usage of each fume hood on campus, which could be used to identify energy-intensive fume hoods. This past semester, they performed thermodynamic calculations in Python using API connections to Cornell’s Energy Management & Control System (EMCS) portal and developed a prototype of the dashboard using the Plotly Dash Python library. Going forward, the team will adapt the dashboard's user interface to client-based needs of 135 Cornell laboratory managers, as well as automate client outreach based on device control, status, and usage.

Things you'll learn: Python, Pandas, Machine Learning, APIs, working with external partners

The SC sub-team aims to reduce errors in Cornell's record of its campus energy consumption by detecting meter faults and anomalies. Previously, SC has focused on quality control, improved baseline algorithms, used ML packages to make the model predictive (e.g., based on weather patterns), collaborated with other faculty and staff working in smart buildings, established methods of communicating when meters need to be fixed, and developed more advanced model selection techniques based on biophysical formulas. Going forward, SC aims to enhance the functionality of its product by incorporating a generative AI-powered chatbot that improves client experience for the model's users. Currently, they are researching LLMs and designing hard-coded prototypes, to prepare for development of the chatbot in future semesters.