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 Photovoltaic an attractive option among various methods of reducing electricity demand. This study aims to apply Machine Learning to predict future impact of solar PV-Generated electricity in reducing CO2 emissions based. 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, therefore reducing electricity consumption to mitigate carbon emissions is paramount. 153 buildings on the campus were investigated, spanning the years 2015-2022. This study was conducted in four key phases. In the first phase, an open source tool, 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 on any period of time, leveraging machine learning to capture complex patterns in energy data for more accurate forecasts. The SHAP (SHapley Additive exPlanations) 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 facilitated the evaluation of different machine learning techniques for projecting emissions and assessing the university’s progress toward Maryland’s sustainability objectives. The ML-based tool developed in this study demonstrated that solar PV implementation could potentially reduce the campus’s footprint by approximately 18% for the studied clusters of buildings with the uncertainty level of about 1.7%, contributing to sustainability objectives and the promotion of cleaner energy use.