Electricity Consumption in North Carolina

About this Challenge

From 9:30 AM to 3:30 PM, find a tabular dataset and make a predictive model with it.

Guidelines to this challenge

Caveats

It is worth noting that I selected North Carolina because North Carolina appears to be the most average State out of the whole United States, based on this 2016 article from Business Insider.

Since completing this Challenge, I have only made the following minor changes:

• Added a PDF version of my unmodified Presentation-Deck (so it can be viewed without download)
• Added the guidelines for this challenge (see link above)
• Added this section to this ReadMe
• Made some minor spelling corrections to this ReadMe
• Changed the Title of this document to be more descriptive.

The rest of this work was completed in about 6 hours.

Contents

• Problem Statement
• Executive Summary
• File Directory
• Data
• Data Dictionary
• Conclusions and Recommendations
• Areas for Further Research/Study
• Sources
• Visualizations

Problem Statement

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Using data from EIA (US Energy Information Administration) are there any patterns in electricity usage in North Carolina from 2001 through 2011, and can we use this data to make a predictive Time-Series Model?

Executive Summary

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Looking at Electrical usage can we forcast future Electrical usage in North Carolina. I selected North Carolina becasue I was looking for a State that would be a good representation of all of the States, and North Carolina seemd to be the most average through my research.

File Directory

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Files should be clearly labeled with descriptive names

03-Project
|
|__ code
|    |__ 00_table_of_contents.ipynb
|    |__ 01_eda_and_cleaning.ipynb
|    |__ 02_null_model.ipynb
|    |__ 03_time_series_models.ipynb
|    |__ 04_auto_regression.ipynb
|    |__ 05_analysis.ipynb
|    |__ 06_conclusion.ipynb
|
|__ data
|    |__ elec_mo_2001_2011_consumption.csv
|
|__ images
|    |__ electric_time_series_null_model.png
|    |__ electric_time_series_seasonal_forecast.png
|    |__ electricity_auto_regression_forecast.png
|    |__ electricity_test_forecast.png
|    |__ electricity_train_test_forecast.png
|
|__ presentation
|    |__ ChrisCaldarella_project4_presentation.pptx
|    |__ ChrisCaldarella_project4_presentation.pdf
|
|__ LICENSE
|__ README.md

Data

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I found some data about Electricity usage on Data Is Plural — Structured Archive document, which led me to the US Energy Information Administration webpage where I found a monthly data set of energy consumption by month over several years.

Data Dictionary

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Feature	Python Type	Data Type	Descritpion
date	DateTime	Continuous	Date; First of the Month
STATE	Object	Nominal	State being observed
CONSUMPTION	float64	Continuous	Amount of total energy consumed that month

Conclusions and Recommendations

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It appears that the Holt-Winters performed the best out of all the models, and fits best when looking at plots. ​ ​ SARIMAX did not do well, but the Seasonal model and the Auto-Regression Models seemed to do ok. ​ ​

Model\Score	RMSE	MAE
Holt-Winters	3631847	2920368
Seasonal	4294624	3603229
Auto-Regression	4414239	3663597
SARIMAX	4713245	3813522
Null Model	5359973	4164017

Areas for Further Research/Study

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We can expand this to other states in the United States.

Sources

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https://www.eia.gov/electricity/data/state/

Visualizations

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