Abstract
This study explores the potential of hybrid solar and wind renewable energy systems in the United States, focusing on two specific locations in the Southwestern region of the country: Vaughn, New Mexico and Fort Stockton, Texas. Using historical wind speed and solar irradiance (GHI) data spanning 2018-2020, I used Python to apply machine learning models and estimate energy output at each site. After testing multiple methods, Random Forest regression emerged as the most reliable approach for capturing and handling the complex seasonal patterns of weather data such as this. The results indicate that both locations demonstrate strong potential for hybrid renewable energy, but Vaughn specifically displays complementary seasonal performance between solar and wind energy output. This research highlights the value of data-driven site evaluation and supports the notion that integrating predictive models can inform more efficient renewable energy investment decisions.
Advisor
Kelvey, Robert
Department
Statistical and Data Sciences
Recommended Citation
Heintz, Andy, "A Data-Driven Approach to Evaluating Hybrid Solar-Wind Systems in the U.S." (2025). Senior Independent Study Theses. Paper 11405.
https://openworks.wooster.edu/independentstudy/11405
Disciplines
Applied Statistics | Longitudinal Data Analysis and Time Series | Statistical Models
Keywords
Renewable Energy, Hybrid, Solar Energy, Wind Energy, Random Forest, Modeling
Publication Date
2025
Degree Granted
Bachelor of Arts
Document Type
Senior Independent Study Thesis
© Copyright 2025 Andy Heintz