Analyzed data from the UCLA Center for Economics and Policy to forecast renewable energy production through
2026.
Utilized Excel and R-code to conduct regression analyses on historical data for hydro, wind, and solar energy generation.
Modeled renewable energy asset life-cycles to create demand cycle forecasts providing valuable insights for future planning and investment opportunities.
LINK
*project was for a UCLA class and was conducted in a group
Utilizing R code and regression analysis I investigated the efficiency of the Los Angeles and energy grid, focusing on weather patterns and energy usage trends.
A data from a variety of sources was overlayed to create a usable data set (CAISO, LDWP, EIA, NOAA, etc). This data set was then analyzed using multiple regression techniques to control for the effects of different variables.
Technical analysis revealed that hotter temperatures and higher humidity levels were associated with lower efficiency.
Additionally, energy usage patterns varied by time of day and day of the week, with certain times being more efficient than others.
The results of this project provide insights into factors impacting energy efficiency in Los Angeles and can inform future efforts to optimize the energy grid.
LINK
*Project was for UCLA class
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