MCM 2018: Interdisciplinary Analysis of Energy Consumption in Various States

Goal of the project

In this project, we aim to provide a mathematical analysis of the energy profiles of certain states in the US based on over 100,000 lines of data provided by the US Energy Information Administration.

Overview of the Paper

The paper describes the energy usage profiles for Arizona (AZ), California (CA), New Mexico (NM), and Texas (TX) from 1960 to 2009. It also provides in-depth explanations of the algorithms, mathematical approaches, and graphs that we developed for the basis for our conclusions.

Overview of the Files

All of the data is in ProblemCData.xlsx.

Each Python file contributes to the overall project in some way, though not all are used for the actual model. The most important files are econModel.py and competeResources.py, which contain a large part of the model itself.

• econModel.py provides the majority of the "Cobb-Douglas Production Function" portion of the program, used to extract certain properties of energy usage.
• competeResources.py utilizes parts of the econModel.py code to gather data about certain properties of energy usage. From there, it will recursively process the existing data (1960-2009) in order to predict energy usage up to 2050.
• rungeKutta.py is used to gather values of energy usage using well-known and accurate approximation methods.
• fetchValues.py parses the provided data and outputs smaller excel files for energy categories we are more concerned about.
• searchData.py searches the data in ProblemCData.xlsx based on a category and year that you input.
• globalFuncs.py and variableFuncs.py provide utility functions.

Running the model

Run python competeResources.py in terminal, and it will output energy consumption for every year, separated by state.

Authors

• Clayton Chu (pacbac) - Creation, Python implementation, and fine-tuning of the model
• Jerry Yin - Creation of the model, finding functions of best fit for data, writing most of the paper
• Zhenxiao Chen - Creation of the model, data analysis, finding functions of best fit for data