This summer, Cesar Rico applied for a competitive slot at the LSU High School Summer Research program and was accepted.  He conducted and presented his research and has provided the following information on the program and his research. 

LSU’s HSSR Program is an internship for high school students to gain real life experience in engineering, computer science, and construction management. Students chose one of the mentors, who are professors at the university, to work with and were given a project in their respective fields. Cesar's mentor was Dr. Amirhosein Jafari in the construction management department, and his project involved a computer analysis of energy burden in low income households. Cesar completed online training, ran analyses with statistical computer software, and created a poster presentation for his project.

"In my time in this program, I gained a thorough understanding of the engineering process and learned techniques in scientific research," Cesar said. "The skills I learned in this program were valuable, and I will continue to use them in years to come."

Here's a glimpse into Cesar's abstract:

44% of households in the U.S. are low-income, and these households often occupy affordable homes, the lowest quality housing unit. As a result, these homes tend to be less energy efficient with a higher energy burden (the percentage of household income spent on energy services). This poses a problem for these low-income households (LIHs) that must pay more for energy. This is especially problematic in the South, where the energy burden can reach over 10% for LIHs. The goal of this project is to analyze the energy characteristics of affordable homes and the energy burden of U.S. LIHs in response to this problem. In this study, the Residential Energy Consumption Survey (RECS) data is used from the U.S. Energy Information Administration (EIA). This data was collected by surveying energy usage and household characteristics from 5,600 homes in the U.S. It has been cleaned by removing imputation flag columns and unavailable data points (marked by a -2), changing certain variables for easier use (ex. converting the range of income into an estimated income), and adding columns for future analysis (energy burden, energy index, poverty line, etc.). The data is then analyzed through JMP using correlation analysis to observe the relationship between the entire data set and three dependent variables: the energy index, the energy burden, and the total energy usage in BTU. A predictive model is also developed using Artificial Neural Network (ANN) to estimate the energy index and energy burden in LIHs.