Event
M.S. Thesis Defense: Sahar Zargarzadeh
Friday, November 8, 2024
12:30 p.m.
AVW 2103 (ISR)
Maria Hoo
301 405 3681
mch@umd.edu
ANNOUNCEMENT: M.S. Thesis Defense
Name: Sahar Zargarzadeh
Committee:
Professor Behtash Babadi, Chair
Professor Michael Ohadi, Co-chair
Professor Amir Shooshtari, Committee member
Date/Time: Friday, November 8, 2024 from 12:30 to 2:00 PM
Location: AVW 2103 (ISR)
Title: ML-Enabled Solar PV Electricity Generation Projection for A Large Academic Campus to Reduce Onsite CO2 Emissions
Abstract: Mitigating CO2 emissions is crucial in reducing climate change, as these emissions contribute to global warming and its adverse impacts on ecosystems. According to statistics, photovoltaic electricity is 15 times less carbon-intensive than natural gas and 30 times less than coal, making solar PV an attractive option among various methods of reducing electricity demand. This study aims to predict the effect of onsite solar photovoltaic-generated electricity on reducing CO2 emissions. The primary utility data source is from the University of Maryland's campus; with over half of the campus's energy consumption derived from electricity, reducing electricity consumption to mitigate carbon emissions is paramount. 153 buildings on the campus were investigated, spanning the years 2015-2023. This study was conducted in four key phases. In the first phase, PVWatts was used to gather data to predict PV-generated energy. This served as the foundation for phase II, where a novel tree-based ensemble learning model was developed to predict monthly PV-generated electricity. The SHAP technique was incorporated into the proposed framework to enhance model explainability. Phase III involved calculating historical CO2 emissions based on past energy consumption data, providing a baseline for comparison. A meta-learning algorithm was implemented in the phase IV to project future CO2 emissions post-solar PV installation. This comparison allowed us to estimate the potential reduction in emissions and to assess the university’s progress toward Maryland’s goal of achieving net-zero emissions. This study's findings suggest that solar PV implementation could reduce the campus’s footprint by around 18% for the studied clusters of buildings, aligning with sustainability objectives and promoting cleaner energy use.
