Date of Graduation
5-2025
Document Type
Thesis
Degree Name
Bachelor of Science in Computer Science
Degree Level
Undergraduate
Department
Computer Science and Computer Engineering
Advisor/Mentor
Le, Ngan
Committee Member
Gauch, John
Second Committee Member
McCann, Roy
Abstract
Solar power is a vital resource in a world being threatened with the ever-evolving impacts of climate change. A combination of new and developing technologies have allowed solar photovoltaic installation to increase at an exponential rate. With this rapid and unprecedented growth comes the task of maintaining tens of thousands of square miles of solar photovoltaic panels. Manually observing and testing solar PV panels for defects or obstructions is costly and time-consuming, distracting valuable resources from the continued installation of new units. This research aims to (i) firstly, introduce a novel dataset on solar PV obstruction, named De-Solar dataset; (ii) secondly, develop a machine learning and computer vision system, SolarFormer++, which is an improvement of SolarFormer [1]; and (iii) finally, we present a multimodal dataset, De-Solar v2.0, which incorporates multispectral imagery from remote sensing and environmental factors.
Keywords
solar PV; computer vision
Citation
Massey, M. (2025). Automated Solar PV Analysis with Machine Learning and Computer Vision: Dataset and Methodology. Electrical Engineering and Computer Science Undergraduate Honors Theses Retrieved from https://scholarworks.uark.edu/elcsuht/20
