The purpose of this project was to use Machine learning models to cast a prediction for the future spending in the residential sector on electricity for the year 2021. Their results will then be compared and a report will be drafted.
EIA's State Energy Data System (SEDS) is a comprehensive data set that consists of annual time series estimates of state-level energy use by major economic sectors, energy production and and State-level energy price and expenditure data. The system provides data back from 1960. Data are presented in physical units, BTUs, and dollars. While some SEDS data series come directly from surveys conducted by EIA, many are estimated using other available information. These estimations are necessary for the compilation of "total energy" estimates.
Useful Links:
The main website: https://catalog.data.gov/dataset/state-energy-data-system-seds
Additional information: https://www.eia.gov/state/seds/
Codes and descriptions: https://www.eia.gov/state/seds/CDF/Codes_and_Descriptions.xlsx
From the State Data energy System description sheets:
The MSNs are five-character codes, most of which are structured as follows:
First and second characters - describes an energy source (for example, NG for natural gas, MG for motor gasoline)
Third and fourth characters - describes an energy sector or an energy activity (for example, RC for residential consumption, PR for production)
Fifth character - describes a type of data (for example, P for data in physical unit, B for data in billion Btu and D is for dollars per million BTU)
The aim of this project is to predict the Primary average price in the residential sector for the year 2021 based on residential sector features.
When loaded the file contains several columns:
Data_Status - A Code
MSN - A 5 letter combination to signify energy source, sector and unit of data.
Units for each category are given below.
Please see :https://www.eia.gov/state/seds/CDF/Codes_and_Descriptions.xlsx for a full description of each category.
- Million Btu per short ton
- Billion Btu
- Dollars per million Btu
- Thousand short tons
- Million dollars
- Thousand barrels
StateCode Two letter state abbreviation
Year YYYY-MM-DD
Data Numerical - associated with MSN
Models tested so far:
- Linear regression Model
- Support Vector Regression Machine
- K Neighbors regressor
- Neural Network model
- Stochastic Gradient Descent
Model Results So far:
The linear model performed very well. With a prediction accuracy of 100%.
The MAE was 0.03267136765242417
The MSE was 0.003015192149111796
The Explained variance was 0.999999999844917
The $ R^2 $ was 0.9999999998350794
The SVR performed very well. With a prediction accuracy of 100%.
The MAE was 0.5021485094357804
The MSE was 0.6832135549229309
The Explained variance was 0.9999999633196209
The $ R^2 $ was 0.999999962630569
The SGDR performed very well. With a prediction accuracy of 100%.
The MAE was 0.9834673843854801
The MSE was 1.5266552927792496
The Explained variance was 0.9999999450342941
The $ R^2 $ was 0.9999999164972077
The k_neighbors performed very well. With a prediction accuracy of 100%.
The MAE was 0.9834673843854801
The MSE was 1.5266552927792496
The Explained variance was 0.9999999450342941
The $ R^2 $ was 0.9999999164972077
The Neural Network Model performed very well. With a prediction accuracy of 100%.
The MAE was 0.032168487841461764
The MSE was 0.0030038938705200698
The Explained variance was 0.9999999998453984
The $ R^2 $ was 0.9999999998356973
The Neural Network Model performed very well. With a prediction accuracy of 100%.
The MAE was 0.032168487841461764
The MSE was 0.0030038938705200698
The Explained variance was 0.9999999998453984
The $ R^2 $ was 0.9999999998356973